2 research outputs found
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Code editing is an essential step towards reliable program synthesis to
automatically correct critical errors generated from code LLMs. Recent studies
have demonstrated that closed-source LLMs (i.e., ChatGPT and GPT-4) are capable
of generating corrective feedback to edit erroneous inputs. However, it remains
challenging for open-source code LLMs to generate feedback for code editing,
since these models tend to adhere to the superficial formats of feedback and
provide feedback with misleading information. Hence, the focus of our work is
to leverage open-source code LLMs to generate helpful feedback with correct
guidance for code editing. To this end, we present Coffee, a collected dataset
specifically designed for code fixing with feedback. Using this dataset, we
construct CoffeePots, a framework for COde Fixing with FEEdback via
Preference-Optimized Tuning and Selection. The proposed framework aims to
automatically generate helpful feedback for code editing while minimizing the
potential risk of superficial feedback. The combination of Coffee and
CoffeePots marks a significant advancement, achieving state-of-the-art
performance on HumanEvalFix benchmark. Codes and model checkpoints are publicly
available at https://github.com/Lune-Blue/COFFEE.Comment: Work in progres
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Human-like chatbots necessitate the use of commonsense reasoning in order to
effectively comprehend and respond to implicit information present within
conversations. Achieving such coherence and informativeness in responses,
however, is a non-trivial task. Even for large language models (LLMs), the task
of identifying and aggregating key evidence within a single hop presents a
substantial challenge. This complexity arises because such evidence is
scattered across multiple turns in a conversation, thus necessitating
integration over multiple hops. Hence, our focus is to facilitate such
multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought
(CoT) reasoning. To this end, we propose a knowledge distillation framework
that leverages LLMs as unreliable teachers and selectively distills consistent
and helpful rationales via alignment filters. We further present DOCTOR, a
DialOgue Chain-of-ThOught Reasoner that provides reliable CoT rationales for
response generation. We conduct extensive experiments to show that enhancing
dialogue agents with high-quality rationales from DOCTOR significantly improves
the quality of their responses.Comment: 25 pages, 8 figures, Accepted to EMNLP 202