Post
135
Exciting Research Alert: Enhancing Dense Retrieval with Deliberate Thinking
I just came across a fascinating new paper titled "Learning More Effective Representations for Dense Retrieval through Deliberate Thinking Before Search" that introduces DEBATER (Deliberate Thinking based Dense Retriever), a novel approach to improve information retrieval using large language models.
The research team from Northeastern University and Tsinghua University has developed a method that significantly outperforms existing dense retrieval systems by enabling LLMs to "think deliberately" before generating document representations.
>> Technical Details
DEBATER enhances LLM-based retrievers through two key mechanisms:
1. Chain-of-Deliberation (CoD): This approach delays the computation of document embeddings by performing several steps of reasoning. It incorporates a sequence of prompt tokens that stimulate the reasoning capability of LLMs, encouraging the model to think step-by-step before producing the final document embedding.
2. Self Distillation (SD): This mechanism distills knowledge from different thinking steps into the final document representation. It identifies the most informative thinking steps and integrates them into a unified text embedding.
The implementation uses cosine similarity to measure the similarity between queries and documents. During training, DEBATER calculates similarity scores between query representation and document representations at each thinking step, then selects the most useful thinking step from CoD.
>> Performance
What's particularly impressive is that DEBATER-4B outperforms larger 7B-scale LLM-based dense retrievers while using significantly fewer parameters. In experiments on the BEIR benchmark, DEBATER achieved more than a 2% improvement over baseline retrievers.
The researchers found that an appropriate thinking depth (around 4-8 steps) effectively activates the reasoning capabilities of LLM-based retrievers.
I just came across a fascinating new paper titled "Learning More Effective Representations for Dense Retrieval through Deliberate Thinking Before Search" that introduces DEBATER (Deliberate Thinking based Dense Retriever), a novel approach to improve information retrieval using large language models.
The research team from Northeastern University and Tsinghua University has developed a method that significantly outperforms existing dense retrieval systems by enabling LLMs to "think deliberately" before generating document representations.
>> Technical Details
DEBATER enhances LLM-based retrievers through two key mechanisms:
1. Chain-of-Deliberation (CoD): This approach delays the computation of document embeddings by performing several steps of reasoning. It incorporates a sequence of prompt tokens that stimulate the reasoning capability of LLMs, encouraging the model to think step-by-step before producing the final document embedding.
2. Self Distillation (SD): This mechanism distills knowledge from different thinking steps into the final document representation. It identifies the most informative thinking steps and integrates them into a unified text embedding.
The implementation uses cosine similarity to measure the similarity between queries and documents. During training, DEBATER calculates similarity scores between query representation and document representations at each thinking step, then selects the most useful thinking step from CoD.
>> Performance
What's particularly impressive is that DEBATER-4B outperforms larger 7B-scale LLM-based dense retrievers while using significantly fewer parameters. In experiments on the BEIR benchmark, DEBATER achieved more than a 2% improvement over baseline retrievers.
The researchers found that an appropriate thinking depth (around 4-8 steps) effectively activates the reasoning capabilities of LLM-based retrievers.
