cs.CLMar 12, 2026
SaveBLooP: Zero-Shot Abstractive Summarization using Large Language Models with Bigram Lookahead Promotion
Organizations: University of Illinois Chicago
Abstract
Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate summaries without fine-tuning, they often miss key details and include extraneous information. We propose BLooP (Bigram Lookahead Promotion), a simple training-free decoding intervention that encourages large language models (LLMs) to generate tokens that form bigrams from the source document. BLooP operates through a hash table lookup at each decoding step, requiring no training, fine-tuning, or model modification. We demonstrate improvements in ROUGE and BARTScore for Llama-3.1-8B-Instruct, Mistral-Nemo-Instruct-2407, and Gemma-2-9b-it on CNN/DM, CCSum, Multi-News, and SciTLDR. Human evaluation shows that BLooP significantly improves faithfulness without reducing readability. We make the code available at https://github.com/varuniyer/BLooP
Figures & tables
| Model Name | Beam width | |
|---|---|---|
| Llama 3.1 8B Instruct | 3 | 12 |
| Mistral Nemo Instruct 2407 | 4 | 5 |
| Gemma 2 9B It | 6 | 4 |
Table 1: Hyper-parameters for each model are selected to maximize BARTScore Yuan et al. (2021) on 10% of CNN/DM’s validation split.
Figure 1: Increasing the beam width consistently improves Llama’s performance on CNN/DM’s validation split. In contrast, Gemma and Mistral stop improving once the beam width exceeds 4 and 5 respectively.
| Win (%) | Tie (%) | Lose (%) | |
|---|---|---|---|
| Faithfulness | 18 | 74 | 8 |
| Informativeness | 48 | 0 | 52 |
| Readability | 0 | 100 | 0 |
Table 4: Human evaluation results (%) comparing Llama 3.1 8B Instruct with and without BLooP on 50 randomly selected examples
| N | with BLooP | P (%) | R (%) | F1 (%) |
|---|---|---|---|---|
| 1 | No | 4.8 | 4.0 | 3.7 |
| 1 | Yes | 4.9 | 2.0 | 2.4 |
| 2 | No | 3.2 | 3.5 | 3.0 |
| 2 | Yes | 3.6 | 1.5 | 1.8 |
| 3 | No | 1.9 | 2.3 | 1.9 |
| 3 | Yes | 2.2 | 1.3 | 1.4 |
Table 5: Novel n-gram precision, recall, and F1 of Llama’s predictions on the CNN/DM test set
Figure 2: Part-of-speech tags of tokens in Llama-generated summaries of CCSum test set articles that differ because of BLooP.
Figure 3: Ablating with Llama 3.1 8B Instruct on 10% of the CNN/DM validation set using a beam size of 8. fw-BLooP is frequency-weighted BLooP, where the promotion in Equation 2 is multiplied by the frequency of the bigram in the input document.
| Dataset | Model | Argmax Change Rate |
|---|---|---|
| CNN/DM | Gemma | 13.67 |
| Llama | 10.43 | |
| Mistral | 12.60 | |
| CCSum | Gemma | 11.03 |
| Llama | 7.18 | |
| Mistral | 10.08 |
Table 6: % of generation steps in which
| Dataset | Model | Cache Hit Rate |
|---|---|---|
| CNN/DM | Gemma | 78.36 |
| + BLooP | 90.46 | |
| Llama | 85.91 | |
| + BLooP | 91.20 | |
| Mistral | 79.31 | |
| + BLooP | 88.32 |
Table 7: % of bigram cache lookups in articles in which
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
| There will be 50 articles paired with 2 summaries each. The task is to select whether summary 1 or 2 is better in faithfulness, informativeness, and readability. The instructions are as follows: 1. Faithfulness : Faithfulness means factual consistency with the context. Please avoid using general knowledge, and only consider it in the context of the provided document. The summary is inconsistent if facts in the summary are not supported by the document. Two typical cases are conflict and hallucination. 2. (a) The summary contradicts the information in the document. The summary might say "A fire broke out in Seattle", but the document says it broke out in Portland. Or the summary might say "the Republicans won the election", but the document indicates the Democrats won instead. (b) The summary adds (hallucinates) a fact that is not mentioned anywhere in the document. For example, the summary might say that "A fire broke out at 2 am", but the document does not mention the time when the fire broke out. 3. Informativeness : It means that a summary expresses the main points of the document. A summary should contain relevant and important information and few unimportant details. If you select the summary to be not consistent with the document, please only consider the consistent information when evaluating this category. 4. Readability : The summaries are written by humans or generated by language models. A summary is readable/fluent if free from language problems. A less readable summary is confusing and difficult to understand. We present an example below. This article reports the accidental death of Alexys Brown. The main information is the accident and the appeal to raise money and minor points can be the investigation, postmortem examination, etc. Summary 1 can be reasonable and acceptable. Summary 2 misses a major point, the appeal, thus not informative. Both summary 3 and summary 4 show unfaithfulness. Summary 3 makes a factual mistake that Alexys died of cancer. This contradicts the article. Summary 4 adds a hallucination that Alexys is three years old. Summary 5 is confusing because grammar flaws impair readability. |
| Article: Alexys Brown, also known as Lexi, died at her home in Emmadale Close, Weymouth, on Thursday. An investigation is under way to discover how she became trapped. A post-mortem examination is due to be carried out this week. It was originally hoped the appeal would raise £2,000. Alison Record, who started the Just Giving appeal, said she was "heart broken" over the death. “Everybody by now has heard of the terrible tragedy the Brown family have suffered with the loss of their beautiful and beloved little girl Lexi,” the appeal page reads. Many other comments have been posted on the appeal page. Steph Harris said: “Thinking of you all at this devastating time, fly high beautiful princess. Love Steph and family xxx” Lesley Andrews added: “No amount of money will take away the pain, but so much love comes with every penny. Take care. xx” Aster Group, the housing association responsible for managing the home, is assisting with the police investigation. The Health and Safety Executive (HSE) is also investigating. Dorset County Council said it had not installed the disabled lift at the property. |
| Summary 1: An appeal to raise money for the family of a girl who died after getting stuck in a lift was originally hoped for raising 2,000 pounds. Summary 2 (informativeness): Alexys Brown, also known as Lexi, died at her home in Emmadale Close, Weymouth, on Thursday. Summary 3 (faithfulness): Alexys Brown, also known as Lexi, died of cancer. The appeal was originally hoped for raising 2,000 pounds. Summary 4 (faithfulness): An appeal to raise money for Alexys Brown, a three-year-old girl who died after getting stuck in a lift was originally hoped for raising 2,000 pounds. Summary 5 (readability): An appeal to raise the family of Alexys Brown became trapped in a lift would raise 2,000 pounds. |
| When scoring summaries, 2 summaries will be provided and the task is to determine which is best (0 if they are tied): |
| CNN/DM Article : Sheffield Wednesday chief executive Paul Aldridge will leave the Championship club at the end of next season. Aldridge, who has previously worked at West Ham, Leicester City and Manchester City, joined the club as vice chairman and CEO in January 2011. Wednesday have brought in Adam Pearson and Glenn Roeder to work alongside head coach Stuart Gray. Sheffield Wednesday chief executive Paul Aldridge (left) will leave the club at the end of next season . Chairman Dejphon Chansiri said the trio would form ’a three-man sporting director by committee’ at Hillsborough. Former Hull City and Derby County owner Pearson will advise on business. Aldridge said: ’I have had a tremendous time at the club and look forward to playing my part next season in what I hope will be a very successful campaign. Glenn Roeder is now part of a new managerial committee team at the Championship outfit . Adam Pearson helped lead Hull to the Premier League and will be hoping he can do the same at Wednesday . ’I am sure by giving the chairman sufficient time to consider who is the right person to lead the club into its 150th anniversary, he will appoint the most suitable successor. ’I know I have a very busy summer ahead, we have at least two major projects starting later this month. Supporters will notice a big difference when they come back at the start of next season and see the new pitch and giant screen. ’I am also hoping to be very busy playing my part in the overhaul of the playing squad, the chairman has made it quite clear he wants to be challenging from the start of the new season.’ Stuart Gray is the manager of the club and has done well since taking over from former boss Dave Jones . |
| Reference Summary : Paul Aldridge joined the club as vice chairman and CEO in January 2011 . Aldridge had before worked at West Ham, Leieceter and Manchester City . Glenn Roeder and Adam Pearson are part of a new management team at Sheffield Wednesday . |
| Llama-3.1-8B-Instruct : Sheffield Wednesday chief executive Paul Aldridge will leave the Championship club at the end of next season. Chairman Dejphon Chansiri has brought in Adam Pearson and Glenn Roeder to work alongside head coach Stuart Gray, forming a ’three-man sporting director by committee’ at Hillsborough. |
| Llama-3.1-8B-Instruct + BLooP : Sheffield Wednesday chief executive Paul Aldridge will leave the Championship club at the end of next season. Aldridge, who has previously worked at West Ham, Leicester City, and Manchester City, joined the club as vice chairman and CEO in January 2011. Chairman Dejphon Chansiri has brought in Adam Pearson and Glenn Roeder to work alongside head coach Stuart Gray. The trio will form ’a three-man sporting director by committee’ at Hillsborough. |
| Mistral-Nemo-Instruct-2407 : Paul Aldridge, Sheffield Wednesday’s CEO since 2011, will depart at the end of next season. The club has appointed Adam Pearson and Glenn Roeder to support head coach Stuart Gray, forming a ’three-man sporting director by committee’. Aldridge, who looks forward to a successful final season, believes chairman Dejphon Chansiri will appoint a suitable successor for the club’s 150th anniversary. |
| Mistral-Nemo-Instruct-2407 + BLooP : Sheffield Wednesday chief executive Paul Aldridge will leave the club at the end of next season. Aldridge, who has previously worked at West Ham, Leicester City, and Manchester City, joined the club as vice chairman and CEO in January 2011. To support head coach Stuart Gray, the club has appointed Adam Pearson and Glenn Roeder to work alongside him, forming a ’three-man sporting director by committee’ at Hillsborough. |
| Gemma-2-9b-it : Sheffield Wednesday chief executive Paul Aldridge will step down at the end of next season. Aldridge, who joined the club in 2011, will work alongside newly appointed Adam Pearson and Glenn Roeder to form a "three-man sporting director by committee." Pearson will advise on business matters, while Roeder joins head coach Stuart Gray in the managerial team. Aldridge expressed confidence in the chairman’s ability to appoint a suitable successor and highlighted upcoming projects, including a new pitch and giant screen, as well as an overhaul of the playing squad. |
| CCSum Article : His visit is coming at a tense moment for Taiwan, China and the U.S. after House Speaker Nancy Pelosi visited Taiwan earlier this month. TAIPEI, Taiwan – Indiana’s Republican governor met with Taiwan’s President Tsai Ing-wen Monday morning, following two recent high-profile visits by U.S. politicians that drew China’s ire and Chinese military drills that included firing missiles over the island. Gov. Eric Holcomb arrived Sunday evening in Taiwan for a four-day visit that will focus on economic exchange, particularly semiconductors, according to a statement from his office. His visit is coming at a tense moment for Taiwan, China and the U.S. after House Speaker Nancy Pelosi visited Taiwan earlier this month. China claims self-ruled Taiwan as its own territory and views exchanges with foreign governments as an infringement on its claims. Tsai acknowledged the tensions in her opening remarks ahead of their meeting Monday morning and welcomed further exchanges. "In the midst of this, Taiwan has been confronted by military threats from China, in and around the Taiwan Strait. At this moment, democratic allies must stand together and boost cooperation in all areas," Tsai said. "Building on our existing foundation of collaboration, I look forward to our supporting one another, and advancing hand in hand, forging closer relations and creating even deeper cooperation." SEE MORE: Diplomatic, military tensions continue to rise in wake of Pelosi’s Taiwan visit In response to Pelosi’s visit, China’s military held several days of exercises that included warplanes flying toward the island and warships sailing across the midline of the Taiwan Strait, an unofficial buffer between the island and mainland. China also imposed visa bans and other sanctions on several Taiwanese political figures, though it’s unclear what effect the sanctions would have. Holcomb emphasized the economic nature of his visit, mentioning that the state is among the top in the U.S. for direct foreign investment and was home to 10 Taiwanese companies. "We both seek to deepen and enhance our already excellent cooperation that we’ve established over the years," he said. Holcomb will also meet representatives of the semiconductor industry, and is expected to promote academic and tech cooperation between Taiwan and the state of Indiana. The delegation is meeting with National Yang-Ming University and National Cheng Kung University as part of the exchange. He is traveling with officials from the state’s economic development council, as well as the dean of engineering at Purdue University, an institution that has just established a semiconductors degree program. He will visit South Korea next. |
| Reference Summary : Indiana’s Republican governor began a visit to Taiwan on Monday, following two recent high-profile visits by U.S. politicians that drew China’s ire and Chinese military drills that included firing missiles over the island. |
| Llama-3.1-8B-Instruct : Indiana’s Republican governor, Eric Holcomb, met with Taiwan’s President Tsai Ing-wen to focus on economic exchange, particularly semiconductors, amidst a tense moment for Taiwan, China, and the U.S. following House Speaker Nancy Pelosi’s visit to Taiwan earlier this month. |
| Llama-3.1-8B-Instruct + BLooP : Indiana’s Republican governor, Eric Holcomb, met with Taiwan’s President Tsai Ing-wen Monday morning, following two recent high-profile visits by U.S. politicians that drew China’s ire and Chinese military drills that included firing missiles over the island. The visit is focused on economic exchange, particularly semiconductors, and is coming at a tense moment for Taiwan, China, and the U.S. after House Speaker Nancy Pelosi’s visit to Taiwan earlier this month. |
| Mistral-Nemo-Instruct-2407 : Indiana’s Republican Governor, Eric Holcomb, arrived in Taiwan for a four-day visit focused on economic exchange, particularly semiconductors, amidst heightened tensions between Taiwan, China, and the U.S. following House Speaker Nancy Pelosi’s visit and China’s subsequent military drills and sanctions. Taiwan’s President Tsai Ing-wen acknowledged the tensions and welcomed further exchanges, while Holcomb emphasized the economic nature of his visit, aiming to deepen cooperation between Indiana and Taiwanese companies. |
| Mistral-Nemo-Instruct-2407 + BLooP : Indiana’s Republican governor, Eric Holcomb, arrived in Taiwan for a four-day visit, focusing on economic exchange, particularly semiconductors, amidst heightened tensions between Taiwan, China, and the U.S. following two recent high-profile visits by U.S. politicians that drew China’s ire and Chinese military drills that included firing missiles over the island. Taiwan’s President Tsai Ing-wen acknowledged the tensions and welcomed further exchanges. Holcomb emphasized the economic nature of his visit, mentioning that the state is among the top in the U.S. for direct foreign investment and was home to 10 Taiwanese companies. He will also meet representatives of the semiconductor industry, and is expected to promote academic and tech cooperation between Taiwan and the state of Indiana. |
| Gemma-2-9b-it : Indiana’s Republican governor, Eric Holcomb, met with Taiwan’s President Tsai Ing-wen during a four-day visit focused on economic exchange, particularly semiconductors. Holcomb’s visit comes amid heightened tensions between Taiwan, China, and the U.S. following House Speaker Nancy Pelosi’s recent trip to Taiwan, which prompted Chinese military drills and sanctions. Tsai acknowledged the tensions and emphasized the need for democratic allies to stand together, while Holcomb stressed the economic nature of his visit, highlighting Indiana’s strong ties with Taiwanese companies and its interest in deepening cooperation. Holcomb’s delegation will meet with semiconductor industry representatives and university officials to promote academic and technological exchange. |
| Multi-News Articles : LONDON (AP) — British restaurant chain Pret a Manger says a second customer has died after eating a sandwich containing an allergen that was not noted on the label. The coffee-and-sandwich business has promised to improve its labeling following criticism at an inquest into the death of 15-year-old Natasha Ednan-Laperouse, who died in 2016 after eating a Pret baguette that contained traces of sesame. The company said an investigation was underway into a second case, in which a customer died in December after eating a supposedly dairy-free product that contained dairy protein. Pret a Manger blamed a supplier of its dairy-free yoghurt. The parents of Ednan-Laperouse, who are campaigning for stronger allergen warnings, said Sunday they were "incredibly saddened to learn of someone else losing their life from allergens in their food." ||||| Pret a Manger has been hit by further controversy over the weekend, as it emerged that a second customer had died after eating one of its sandwiches in 2017. |
| The sandwich chain found itself in a escalating row with a former supplier, after it blamed the second death on an ingredient supplied by the vegan brand CoYo, a claim the yoghurt company said was unfounded. It emerged on Saturday that a Pret customer had died in 2017 after eating a “super-veg rainbow flatbread” that was supposed to be dairy-free. Pret said it had been mis-sold yoghurt that forms one of the ingredients of the flatbread and that was guaranteed dairy-free but was found to contain dairy protein. The company agreed to full labelling of ingredients on all its freshly made products last week following the case of 15-year-old Natasha Ednan-Laperouse, who was allergic to sesame and died on a flight after eating one of its baguettes bought at Heathrow airport. Following the news of another death, Natasha’s parents, Nadim and Tanya Ednan-Laperouse, said: “We were incredibly saddened to learn of someone else losing their life from allergens in their food. Our hearts go out to the bereaved family.” The pair have been campaigning for allergy awareness and a change in food-labelling laws. Pret a Manger to bring in full labelling after teenager’s death Read more The second customer, who has not been named, collapsed and died on 27 December after buying the sandwich in a shop in Stall Street, Bath. Bath’s council alerted Pret to the incident and the chain said it withdrew all affected products. The firm said it had ended its contract with CoYo and was taking legal action. “Subsequent testing by Pret and two independent authorities found that the CoYo dairy-free yoghurt contained traces of dairy protein,” a Pret spokesman said. “This is believed to have resulted in the tragic death of a customer from an allergic reaction in December 2017. “Our deepest sympathies are with the family and friends of our customer in this terrible case and we will seek to assist them in any way we can.” CoYo, a coconut milk brand that the TV cook Nigella Lawson has endorsed, said on Sunday that Pret’s claims that it was to blame were unfounded. It accused the sandwich chain of hampering its own investigation into the death by failing to provide vital information. “Pret’s inability to provide us with a batch code, despite several requests, has severely limited our ability to investigate this further,” it said CoYo recalled its yoghurts in February 2018 after dairy traces were found. The Food Standards Agency investigated together with Bexley council in London, where CoYo is based, before it issued an allergy alert. CoYo denied on Sunday that the product recall was related to the death. “The dairy-free product we provided to Pret in December 2017, at the time of this tragedy, is not linked to the product we recalled in February 2018,” a spokeswoman said. The company would continue to help to find the true cause of the death, she said. “We urge all parties to work together, and not to speculate on the cause of this tragic death that is unknown as far as we are aware and is still being investigated by the coroner’s court.” Following the February recall, Coyo said it had been devastated to discover dairy in its yoghurts, “especially as being dairy-free sits at the heart of everything we do”. It said it had carried out a “thorough review” of its ingredients and identified that the contaminated material was provided by an external third-party supplier, whose relationship had now been terminated. |
| Reference Summary : – British restaurant chain Pret a Manger says a second customer has died after eating a sandwich containing an allergen that was not noted on the label, reports the AP. The coffee-and-sandwich business said an investigation was underway into the second case, in which a customer died December 27 after eating a supposedly dairy-free "super-veg rainbow flatbread" that contained dairy protein, reports the Guardian. "This is believed to have resulted in the tragic death of a customer from an allergic reaction," says a Pret a Manger spokesperson, and blaming CoYo, a supplier of its dairy-free yogurt. Pret a Manger has promised to improve its labeling following criticism at an inquest into the death of 15-year-old Natasha Ednan-Laperouse, who died in 2016 after eating a Pret baguette that contained traces of sesame. Natasha’s parents, who are campaigning for stronger allergen warnings, said Sunday they were "incredibly saddened to learn of someone else losing their life from allergens in their food." |
| Llama-3.1-8B-Instruct : A second customer has died after eating a Pret a Manger sandwich that contained an allergen not noted on the label. The customer, who has not been named, collapsed and died on December 27, 2017, after eating a "super-veg rainbow flatbread" that was supposed to be dairy-free. An investigation found that the flatbread contained dairy protein, which is believed to have caused the customer’s death. Pret a Manger has ended its contract with the supplier of the dairy-free yoghurt and is taking legal action. |
| Llama-3.1-8B-Instruct + BLooP : A second customer has died after eating a Pret a Manger sandwich containing an allergen that was not noted on the label. The customer, who has not been named, collapsed and died on December 27, 2017, after eating a supposedly dairy-free product that contained dairy protein. Pret a Manger has promised to improve its labeling following criticism at an inquest into the death of 15-year-old Natasha Ednan-Laperouse, who died in 2016 after eating a Pret baguette that contained traces of sesame. |
| Mistral-Nemo-Instruct-2407 : Pret a Manger, a British restaurant chain, is facing another allergen-related death, the second in two years. In December 2017, a customer died after consuming a "super-veg rainbow flatbread" that was supposed to be dairy-free but contained dairy protein due to a mislabeled ingredient from supplier CoYo. Pret has ended its contract with CoYo and is taking legal action. This incident follows the death of 15-year-old Natasha Ednan-Laperouse in 2016 from an allergic reaction to sesame in a Pret baguette, leading the company to promise improved labeling. |
| Mistral-Nemo-Instruct-2407 + BLooP : A second customer has died after eating a Pret a Manger sandwich containing an allergen that was not noted on the label. This follows the death of 15-year-old Natasha Ednan-Laperouse, who died in 2016 after eating a Pret baguette that contained traces of sesame. Pret a Manger has promised to improve its labeling following criticism at an inquest into Ednan-Laperouse’s death. The second death, in December 2017, involved a customer who died after eating a supposedly dairy-free product that contained dairy protein. Pret a Manger blamed a supplier of its dairy-free yoghurt. The parents of Ednan-Laperouse, who are campaigning for stronger allergen warnings, said they were "incredibly saddened to learn of someone else losing their life from allergens in their food." |
| SciTLDR AIC : Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attentional mechanisms into neural networks increases the performance of the system substantially. We report on a new modular network architecture that applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. This network called the sensor transformation attention network (STAN) is evaluated in scenarios that include the presence of natural noise or synthetic dynamic noise. We demonstrate how the attentional signal responds dynamically to changing noise levels and sensor-specific noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The improvement grows as more channels are corrupted as demonstrated on the CHiME-3 dataset. Moreover, the proposed STAN architecture naturally introduces a number of advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments. Attentional mechanisms have shown improved performance as part of the encoder-decoder based sequence-to-sequence framework for applications such as image captioning BID22 , speech recognition BID1 BID3 , and machine translation BID0 BID21 . Dynamic and shifting attention, for example, on salient attributes within an image helps in image captioning as demonstrated by the state-of-art results on multiple benchmark datasets BID22 . Similarly, an attention-based recurrent sequence generator network can replace the Hidden Markov Model (HMM) typically used in a large vocabulary continuous speech recognition system, allowing an HMM-free RNN-based network to be trained for end-to-end speech recognition BID1 .While attentional mechanisms have mostly been applied to both spatial and temporal features, this work focuses on attention used in sensor selection. We introduce the STAN architecture that embeds an attentional mechanism for sensor selection and supports multi-sensor as well as multi-modal inputs. This attentional mechanism allows STANs to dynamically focus on sensors with higher signal-to-noise ratio (SNR) and its output is highly interpretable. Because of their inherently modular architecture, STANs remain operational even when sensors are removed after training. The same modularity makes STANs attractive for tasks that make use of multi-sensor integration. The STAN architecture can be seen as a generalization of existing less-modular network types that include attention in multi-sensor setups BID11 BID10 .This work consists of three main sections. First, we formally introduce the STAN architecture in section 2. In the first evaluation phase in section 3, we demonstrate the proper function of the attentional mechanism in synthetic noise environments with multiple audio sensors (TIDIGITS dataset) and multiple video sensors (GRID). The second evaluation phase in section 4 covers the multi-microphone real world dataset CHiME-3, where we show the proper functioning of the STAN attentional mechanism on natural noise and the robustness of STANs with respect to altered sensor configurations. The sensor |
| transformation attention network (STAN) architecture has a number of interesting features for sensor selection that we explored in this work. By equipping each sensor with an attentional mechanism for distinguishing meaningful features, networks can learn how to select, transform, and interpret the output of their sensors. Firstly, and by design, we show that STANs exhibit remarkable robustness to both real-world and synthetic dynamic noise sources. By challenging these networks during training with dynamic and persistent noise sources, the networks learn to rapidly isolate sensors corrupted by noise sources. Secondly, we show that this form of training results in even better accuracy performance from STANs than simply concatenating the sensor inputs. This is best demonstrated on the heavily noise corrupted STR environment of the CHiME-3 real-data evaluation set, where STANs achieve 23% lower WER than concatenation models for the 50 most corrupted samples. Thirdly, we find that the output of the attention modules is highly informative, clearly indicating a sub-optimal sensor placement for a sensor pointing away from the speaker on the CHiME-3 dataset. Remarkably, this outcome is even obtained when sharing the weights of the attention modules across sensors, implying that these attention modules learned to successfully differentiate between sensors with higher and lower SNR data in presence of natural noise.Due to their modular architecture, STANs are also remarkably flexible with respect to the sensor configuration, even performing well with the removal of sensors after training. One can train STANs to solve a task with a multi-sensor setup and after training, remove the less informative sensors, with possibly savings of energy consumption and computational load on multi-sensor hardware systems with restricted computational power such as mobile robots. In the case of a defect, a sensor could be removed and STANs would remain operational with the remaining sensors. |
| Reference Summary : We introduce a modular multi-sensor network architecture with an attentional mechanism that enables dynamic sensor selection on real-world noisy data from CHiME-3. A generic neural architecture able to learn the attention that must be payed to different input channels depending on the relative quality of each sensor with respect to the others. Considers the use of attention for sensor or channel selection with results on TIDIGITS and GRID showing a benefit of attention over concatentation of features. |
| Llama-3.1-8B-Instruct : Researchers have developed a new neural network architecture called the sensor transformation attention network (STAN), which applies an attentional mechanism to sensor selection for multi-sensor setups. The STAN architecture is evaluated in scenarios with natural and synthetic noise, and it is shown to reduce word error rates on audio and visual tasks. The attentional signal responds dynamically to changing noise levels and sensor-specific noise, leading to improved performance. The STAN architecture also introduces advantages such as ease of removing sensors, attentional interpretability, and increased robustness to various noise environments. |
| Llama-3.1-8B-Instruct + BLooP : Researchers have developed a new modular network architecture called the sensor transformation attention network (STAN) that applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. The STAN architecture has shown improved performance in scenarios with natural noise or synthetic dynamic noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The proposed STAN architecture naturally introduces a number of advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments. |
| Mistral-Nemo-Instruct-2407 : The article presents a novel neural network architecture, Sensor Transformation Attention Network (STAN), which integrates an attentional mechanism for sensor selection in multi-sensor setups. Unlike existing models that focus on temporal and spatial attention, STAN dynamically allocates attention to sensors with higher signal-to-noise ratio (SNR), improving performance in noisy environments. Evaluated on audio (TIDIGITS) and visual (GRID) datasets with synthetic noise, and a real-world multi-microphone dataset (CHiME-3) with natural noise, STAN reduced word error rates (WERs) and maintained robustness with altered sensor configurations. The modular architecture of STAN allows for easy removal of sensors post-training, offering interpretability and increased robustness to various noise environments. |
| Mistral-Nemo-Instruct-2407 + BLooP : The article introduces a new modular network architecture, the Sensor Transformation Attention Network (STAN), which applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. Evaluated in scenarios with natural noise or synthetic dynamic noise, STAN dynamically responds to changing noise levels and sensor-specific noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The improvement grows as more channels are corrupted as demonstrated on the CHiME-3 dataset. The proposed STAN architecture naturally introduces advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments. |
| SciTLDR AIC : Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model that extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models. Source code is publicly available at https://imatge-upc.github.io/skiprnn-2017-telecombcn/. Recurrent Neural Networks (RNNs) have become the standard approach for practitioners when addressing machine learning tasks involving sequential data. Such success has been enabled by the appearance of larger datasets, more powerful computing resources and improved architectures and training algorithms. Gated units, such as the Long Short-Term Memory (Hochreiter & Schmidhuber, 1997 ) (LSTM) and the Gated Recurrent Unit (Cho et al., 2014 ) (GRU), were designed to deal with the vanishing gradients problem commonly found in RNNs (Bengio et al., 1994) . These architectures have been popularized, in part, due to their impressive results on a variety of tasks in machine translation (Bahdanau et al., 2015) , language modeling BID17 and speech recognition (Graves et al., 2013) .Some of the main challenges of RNNs are in their training and deployment when dealing with long sequences, due to their inherently sequential behaviour. These challenges include throughput degradation, slower convergence during training and memory leakage, even for gated architectures (Neil et al., 2016) . Sequence shortening techniques, which can be seen as a sort of conditional computation (Bengio et al., 2013; Bengio, 2013; Davis & Arel, 2013) in time, can alleviate these issues. The most common approaches, such as cropping discrete signals or reducing the sampling rate in continuous signals, are based |
| on heuristics and can be suboptimal. In contrast , we propose a model that is able to learn that samples (i.e., elements in the input sequence) need to be used in order to solve the target task. Consider a video understanding task as an example: scenes with large motion may benefit from high frame rates, whereas only a few frames are needed to capture the semantics of a mostly static scene.The main contribution of this work is a novel modification for existing RNN architectures that allows them to skip state updates, decreasing the number of sequential operations performed, without requiring any additional supervision signal. This model , called Skip RNN, adaptively determines whether the state needs to be updated or copied to the next time step. We show how the network can be encouraged to perform fewer state updates by adding a penalization term during training, allowing us to train models under different computation budgets. The proposed modification can generally be integrated with any RNN and we show, in this paper, implementations with well-known RNNs, namely LSTM and GRU. The resulting models show promising results on a series of sequence modeling tasks. In particular , we |
| evaluate the proposed Skip RNN architecture on six sequence learning problems: an adding task, sine wave frequency discrimination, digit classification, sentiment analysis in movie reviews, action classification in video, and temporal action localization in video 1 . We presented Skip RNNs as an extension to existing recurrent architectures enabling them to skip state updates thereby reducing the number of sequential operations in the computation graph. Unlike other approaches, all parameters in Skip RNN are trained with backpropagation. Experiments conducted with LSTMs and GRUs showed that Skip RNNs can match or in some cases even outperform the baseline models while relaxing their computational requirements. Skip RNNs provide faster and more stable training for long sequences and complex models, owing to gradients being backpropagated through fewer time steps resulting in a simpler optimization task. Moreover, the introduced computational savings are better suited for modern hardware than those methods that reduce the amount of computation required at each time step (Koutnik et al., 2014; Neil et al., 2016; Chung et al., 2017) .A ADDITIONAL EXPERIMENTS |
| Reference Summary : A modification for existing RNN architectures that allows them to skip state updates while preserving the performance of the original architectures. Proposes the Skip RNN model that allows a recurrent network to selectively skip updating its hidden state for some inputs, leading to reduced computation at test-time. Proposes a novel RNN model where both the input and the state update of the recurrent cells are skipped adaptively for some time steps. |
| Llama-3.1-8B-Instruct : Recurrent Neural Networks (RNNs) have shown outstanding performance in sequence modeling tasks, but training them on long sequences can be challenging due to slow inference, vanishing gradients, and difficulty in capturing long-term dependencies. To address these issues, a new model called Skip RNN is introduced, which extends existing RNN models by learning to skip state updates and shortening the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint, resulting in faster and more stable training for long sequences and complex models. |
| Llama-3.1-8B-Instruct + BLooP : Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. To address these issues, the Skip RNN model is introduced, which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models. |
| Mistral-Nemo-Instruct-2407 : The article introduces the Skip RNN, a modification to existing Recurrent Neural Networks (RNNs) that allows them to skip state updates, reducing the number of sequential operations performed. This model adaptively decides whether to update or copy the state to the next time step, enabling it to handle long sequences more efficiently. The Skip RNN can be integrated with popular RNN architectures like LSTM and GRU, and its performance is evaluated on six sequence learning tasks. Results show that Skip RNN can match or even outperform baseline models while reducing computational requirements, leading to faster and more stable training for long sequences and complex models. The source code is publicly available. |
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