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

• Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies [10.971976066073442]
  Speculative decoding (SD) methods offer substantial efficiency gains by generating multiple tokens using a single target forward pass. Existing SD approaches require the drafter and target models to share the same vocabulary, thus limiting the pool of possible drafters. We present three new SD methods that remove this shared-vocabulary constraint.
  arXiv  Detail & Related papers  (2025-01-31T19:13:58Z)
• Real-time Verification and Refinement of Language Model Text Generation [60.04718679054704]
  Large language models (LLMs) have shown remarkable performance across a wide range of natural language tasks. A critical challenge remains in that they sometimes generate factually incorrect answers. We propose Streaming-VR, a novel approach designed to enhance the efficiency of verification and refinement of LLM outputs.
  arXiv  Detail & Related papers  (2025-01-14T03:59:48Z)
• 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)
• Learnable Item Tokenization for Generative Recommendation [78.30417863309061]
  We propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), which integrates hierarchical semantics, collaborative signals, and code assignment diversity. LETTER incorporates Residual Quantized VAE for semantic regularization, a contrastive alignment loss for collaborative regularization, and a diversity loss to mitigate code assignment bias.
  arXiv  Detail & Related papers  (2024-05-12T15:49:38Z)
• 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.