Related papers: Best Practices for Distilling Large Language Models into BERT for Web Search Ranking

Best Practices for Distilling Large Language Models into BERT for Web Search Ranking

URL: http://arxiv.org/abs/2411.04539v1
Date: Thu, 07 Nov 2024 08:54:46 GMT
Title: Best Practices for Distilling Large Language Models into BERT for Web Search Ranking
Authors: Dezhi Ye, Junwei Hu, Jiabin Fan, Bowen Tian, Jie Liu, Haijin Liang, Jin Ma,
Abstract summary: Large Language Models (LLMs) can generate a ranked list of potential documents. We transfer the ranking expertise of LLMs to a more compact model like BERT, using a ranking loss to enable the deployment of less resource-intensive models. Our model has been successfully integrated into a commercial web search engine as of February 2024.
Score: 14.550458167328497
License:
Abstract: Recent studies have highlighted the significant potential of Large Language Models (LLMs) as zero-shot relevance rankers. These methods predominantly utilize prompt learning to assess the relevance between queries and documents by generating a ranked list of potential documents. Despite their promise, the substantial costs associated with LLMs pose a significant challenge for their direct implementation in commercial search systems. To overcome this barrier and fully exploit the capabilities of LLMs for text ranking, we explore techniques to transfer the ranking expertise of LLMs to a more compact model similar to BERT, using a ranking loss to enable the deployment of less resource-intensive models. Specifically, we enhance the training of LLMs through Continued Pre-Training, taking the query as input and the clicked title and summary as output. We then proceed with supervised fine-tuning of the LLM using a rank loss, assigning the final token as a representative of the entire sentence. Given the inherent characteristics of autoregressive language models, only the final token </s> can encapsulate all preceding tokens. Additionally, we introduce a hybrid point-wise and margin MSE loss to transfer the ranking knowledge from LLMs to smaller models like BERT. This method creates a viable solution for environments with strict resource constraints. Both offline and online evaluations have confirmed the efficacy of our approach, and our model has been successfully integrated into a commercial web search engine as of February 2024.

Related papers

RRADistill: Distilling LLMs' Passage Ranking Ability for Document Re-Ranking of Long-Tail Queries in a Search Engine [2.0379810233726126]
Large Language Models (LLMs) excel at understanding the semantic relationships between queries and documents. These queries are challenging for feedback-based rankings due to sparse user engagement and limited feedback. We propose an efficient label generation pipeline and novel sLLM training methods for both encoder and decoder models.
arXiv Detail & Related papers (2024-10-08T11:28:06Z)
Soft Prompting for Unlearning in Large Language Models [11.504012974208466]
This work focuses on investigating machine unlearning for Large Language Models motivated by data protection regulations. We propose a framework textbfSoft textbfPrompting for textbfUntextbflearning (SPUL) We conduct a rigorous evaluation of the proposed method and our results indicate that SPUL can significantly improve the trade-off between utility and forgetting.
arXiv Detail & Related papers (2024-06-17T19:11:40Z)
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization [12.885866125783618]
Large Language Models (LLMs) tend to produce inaccurate responses to specific queries. We construct an adversarial dataset, named as $textbfADT (Adrial dataset for Tokenizer)$ to challenge LLMs' tokenization. Our empirical results reveal that our ADT is highly effective on challenging the tokenization of leading LLMs, including GPT-4o, Llama-3, Qwen2.5-max and so on.
arXiv Detail & Related papers (2024-05-27T11:39:59Z)
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning [35.03338699349037]
We propose a novel in-context learning framework, FeatLLM, which employs Large Language Models as feature engineers. FeatLLM generates high-quality rules, significantly (10% on average) outperforming alternatives such as TabLLM and STUNT.
arXiv Detail & Related papers (2024-04-15T06:26:08Z)
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
arXiv Detail & Related papers (2024-01-02T18:53:13Z)
Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
arXiv Detail & Related papers (2023-12-26T07:24:46Z)
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
arXiv Detail & Related papers (2023-10-10T16:38:49Z)
Label Supervised LLaMA Finetuning [13.939718306233617]
In this paper, we introduce a label-supervised adaptation for Large Language Models (LLMs) We extract latent representations from the final LLaMA layer and project them into the label space to compute the cross-entropy loss. Remarkably, without intricate prompt engineering or external knowledge, LS-LLaMA substantially outperforms LLMs ten times its size in scale.
arXiv Detail & Related papers (2023-10-02T13:53:03Z)
Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
arXiv Detail & Related papers (2023-09-20T09:23:46Z)
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models [73.86954509967416]
Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks. This paper presents the first comprehensive MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks.
arXiv Detail & Related papers (2023-06-23T09:22:36Z)
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents [56.104476412839944]
Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization across various language-related tasks. This paper investigates generative LLMs for relevance ranking in Information Retrieval (IR) To address concerns about data contamination of LLMs, we collect a new test set called NovelEval. To improve efficiency in real-world applications, we delve into the potential for distilling the ranking capabilities of ChatGPT into small specialized models.
arXiv Detail & Related papers (2023-04-19T10:16:03Z)

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