Papers with small-scale language models
Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning (2024.acl-long)
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| Challenge: | Recent studies focus on enhancing large-scale language models' reasoning abilities, but the research question of how to GSM8K Performance vs. computational cost remains. |
| Approach: | They propose to train small-scale language models with their own outputs to avoid relying on large models' outputs. |
| Outcome: | The proposed approach outperforms baseline models with comparable sizes while minimizing the required compute. |
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering (2024.lrec-main)
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| Challenge: | Large language models have demonstrated remarkable capabilities by leveraging chain-of-thought reasoning techniques to solve complex questions. |
| Approach: | They propose a method that assesses the entailment relationship between the question and the candidate reasoning chain and uses it to predict the answer. |
| Outcome: | The proposed approach improves the fine-tuned T5 baseline over the ScienceQA, ECQA, and LastLetter tasks. |
Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks (2025.emnlp-main)
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| Challenge: | Existing methods for retrieval of information excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks. |
| Approach: | They propose a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. |
| Outcome: | The proposed model outperforms existing models on a BRIGHT benchmark with BM25 retrievers. |