Summary of Position Debiasing Fine-tuning For Causal Perception in Long-term Dialogue, by Shixuan Fan et al.
Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogue
by Shixuan Fan, Wei Wei, Wendi Li, Xian-Ling Mao, Wenfeng Xie, Dangyang Chen
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method, called Causal Perception long-term Dialogue framework (CPD), to alleviate position bias in dialogue generation models. Position bias occurs when models focus on nearby utterances instead of causally relevant ones, resulting in irrelevant and generic responses. CPD employs perturbation-based causal variable discovery to extract causally relevant utterances and enhance model causal perception during fine-tuning. The method includes a local-position awareness mechanism for inter-sentence position correlation elimination and a casual-perception fine-tuning strategy to discover causal invariant factors. Experimental results on two datasets show that CPD can effectively alleviate position bias for multiple large language models (LLMs) and outperform existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with dialogue generation models, which often respond in an unhelpful way due to a natural flaw called position bias. Position bias happens when the model focuses on what was just said instead of what’s truly important for responding. To fix this, the authors created a new method called CPD that uses special perturbations to find the most relevant parts of the conversation and train the model to respond better. |
Keywords
» Artificial intelligence » Fine tuning
