Dictionary-like definition generators are known to be useful for lexical semantic change detection (LSCD). However, they suffer from overly granular sense distribution, producing excessive number of definitions for one and the same lexical sense. In this work, we show that fine-tuning with Direct Preference Optimization (DPO) helps to overcome this problem. We test our approach both quantitatively on a curated academic LSCD benchmark and qualitatively on real-world data for a practical task of auditing diachronic changes in sustainability terminology within project documentation of EU development finance. All models, code and predictions produced in this study are publicly available at https://github.com/ltgoslo/dpo-for-definition-generation.
Figures & tables
Split
Target words
Examples
SFT train
90 030
220 325
SFT dev
5 144
14 793
DPO train
90 030
220 540
DPO dev
5 145
14 407
Table 1: DBnary data split statistics before post-processing the DPO split.
Parameter
SFT
DPO
LoRA rank
256
64
LoRA alpha
512
16
LoRA dropout
0.1
0.2
learning rate schedule
linear
linear
learning rate
2e-5
1e-6
batch size
32
24
Table 2: Training hyperparameters.
Model
mT0-xl SFT
Llama3Dictionary SFT
LlamaDictionary-ML38B SFT
OLMo-3 SFT
LlamaDictionary-ML38B DPO
OLMo-3 DPO
No merging
0.476 [0.18, 0.69]
0.429 [0.12, 0.66]
0.517 [0.23, 0.72]
0.586 [0.32, 0.76]
0.532 [0.25, 0.73]
0.546 [0.27, 0.74]
Minimalist merging 50
0.565
0.547
0.237
0.445
0.354
0.258
Full merging 50
0.493
0.494
0.200
0.389
0.224
0.170
Full merging 22 (tuned)
0.431
0.510
0.444
0.562
0.426
0.380
Clustering
0.435
0.465
0.464
0.557
0.493
0.498
Clustering separately
0.171
0.214
0.42
0.207
0.458
0.068
Table 3: Evaluation on SemEval’20 Task 1, Spearman rank correlations. Generated definitions were obtained in single runs, and sense detection algorithms are deterministic. Confidence intervals for the best performing postprocessing (no postprocessing) are reported in brackets.
Model
Total senses
Senses per word
% amb.
Senses per amb.
Human (DBnary gold)
7 645
1.49
20.1
6.76
Llama ML38B SFT
9 228
1.79
30.2
5.50
Llama ML38B DPO
9 044
1.76
29.3
5.58
OLMo-3 SFT
12 721
2.47
46.7
4.68
OLMo-3 DPO
12 143
2.36
45.3
4.72
Table 4: Distribution of senses in the DBnary development set and in the predictions of our models. ‘Amb.’ stands for ‘ambiguous words’ (more than one sense).
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
APD value
mean
0.13
std
0.10
min
0.0
25%
0.07
50%
0.11
75%
0.17
Appendix
Table 5: Descriptive statistics of APD calculated by XL-Durel.