Papers with tuning models
Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to Boost for Reasoning (2024.acl-long)
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| Challenge: | Recent studies have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought rationales or using them as correct examples in few-shot prompting. |
| Approach: | They propose a new benchmark to test the effectiveness of large language models by leveraging errors to enhance reasoning capabilities. |
| Outcome: | The proposed methods can be used to fine-tune models in correct and incorrect domains, rather than tuning models to learn ground truth in traditional methods. |
Machine Translation Robustness to Natural Asemantic Variation (2022.emnlp-main)
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| Challenge: | Existing machine translation models struggle with noisy data and tail-end words and phrases. |
| Approach: | They introduce and formalize a class of noise and variation that preserves meaning in the target language. |
| Outcome: | The proposed model can perform better on natural asemantic variation (NAV) the proposed model is robust to a variety of perturbations, but not all of them are achieved with organic variations. |
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be prohibitive in terms of feasibility, time, and resources. |
| Approach: | They propose a method for training large language models by enabling "self-talk" they propose supervised fine-tuning of LLMs to improve quality of dialogues . |
| Outcome: | The proposed method generates training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. |