Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation

• URL: http://arxiv.org/abs/2406.14900v3
• Date: Tue, 05 Nov 2024 06:52:30 GMT
• Title: Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation
• Authors: Keqin Bao, Jizhi Zhang, Yang Zhang, Xinyue Huo, Chong Chen, Fuli Feng,
• Abstract summary: We introduce a new decoding approach named Debiasing-Diversifying Decoding (D3) D3 disables length normalization for ghost tokens to alleviate amplification bias. Experiments on real-world datasets demonstrate the method's effectiveness.
• Score: 32.85339480783571
• License:
• Abstract: Adapting Large Language Models (LLMs) for recommendation requires careful consideration of the decoding process, given the inherent differences between generating items and natural language. Existing approaches often directly apply LLMs' original decoding methods. However, we find these methods encounter significant challenges: 1) amplification bias -- where standard length normalization inflates scores for items containing tokens with generation probabilities close to 1 (termed ghost tokens), and 2) homogeneity issue -- generating multiple similar or repetitive items for a user. To tackle these challenges, we introduce a new decoding approach named Debiasing-Diversifying Decoding (D3). D3 disables length normalization for ghost tokens to alleviate amplification bias, and it incorporates a text-free assistant model to encourage tokens less frequently generated by LLMs for counteracting recommendation homogeneity. Extensive experiments on real-world datasets demonstrate the method's effectiveness in enhancing accuracy and diversity. The code is available at https://github.com/SAI990323/DecodingMatters.

Related papers

• $\mathbb{USCD}$: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding [64.00025564372095]
  Large language models (LLMs) have shown remarkable capabilities in code generation. The effects of hallucinations (e.g., output noise) make it challenging for LLMs to generate high-quality code in one pass. We propose a simple and effective textbfuncertainty-aware textbfselective textbfcontrastive textbfdecoding.
  arXiv  Detail & Related papers  (2024-09-09T02:07:41Z)
• SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation [35.10931307279044]
  This paper proposes Self-Evaluation Decoding, SED, a decoding method for enhancing model generation. It integrates speculation and evaluation steps into the decoding process, allowing LLMs to make more careful decisions.
  arXiv  Detail & Related papers  (2024-05-26T12:43:18Z)
• HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models [81.56455625624041]
  We introduce the first open-source benchmark to utilize external large language models (LLMs) for ASR error correction. The proposed benchmark contains a novel dataset, HyPoradise (HP), encompassing more than 334,000 pairs of N-best hypotheses. LLMs with reasonable prompt and its generative capability can even correct those tokens that are missing in N-best list.
  arXiv  Detail & Related papers  (2023-09-27T14:44:10Z)
• Contrastive Decoding Improves Reasoning in Large Language Models [55.16503283583076]
  We show that Contrastive Decoding achieves large out-of-the-box improvements over greedy decoding on a variety of reasoning tasks. We show that Contrastive Decoding leads LLaMA-65B to outperform LLaMA 2, GPT-3.5 and PaLM 2-L on the HellaSwag commonsense reasoning benchmark.
  arXiv  Detail & Related papers  (2023-09-17T00:29:32Z)
• Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models [54.72004797421481]
  We conduct the first systematic study to explore a decoding strategy specialized in code generation. Inspired by the above findings, we propose a simple yet effective method: Adaptive Temperature (AdapT) sampling. Results show that AdapT sampling significantly outperforms state-of-the-art decoding strategy.
  arXiv  Detail & Related papers  (2023-09-06T06:27:33Z)
• Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation [92.42032403795879]
  We show that pretrained language models (LMs) such as GPT2 still tend to generate repetitive texts. We attribute their overestimation of token-level repetition probabilities to the learning bias. We find that LMs use longer-range dependencies to predict repetitive tokens than non-repetitive ones, which may be the cause of sentence-level repetition loops.
  arXiv  Detail & Related papers  (2023-07-04T07:53:55Z)
• Contrastive Decoding: Open-ended Text Generation as Optimization [153.35961722855686]
  We propose contrastive decoding (CD), a reliable decoding approach. It is inspired by the fact that the failures of larger LMs are even more prevalent in smaller LMs. CD requires zero additional training, and produces higher quality text than decoding from the larger LM alone.
  arXiv  Detail & Related papers  (2022-10-27T00:58:21Z)
• Sparse Coding with Multi-Layer Decoders using Variance Regularization [19.8572592390623]
  We propose a novel sparse coding protocol which prevents a collapse in the codes without the need to regularize the decoder. Our method regularizes the codes directly so that each latent code component has variance greater than a fixed threshold. We show that sparse autoencoders with multi-layer decoders trained using our variance regularization method produce higher quality reconstructions with sparser representations.
  arXiv  Detail & Related papers  (2021-12-16T21:46:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.