통찰 - Open-domain question answering - # Retrieval Augmented Question Answering

Trustworthy Retrieval Augmented Question Answering with Conformal Prediction

Q: How can TRAQ be extended to handle cases where the underlying retriever and language model do not perform well?

In cases where the underlying retriever and language model do not perform well, TRAQ can be extended by implementing additional strategies to ensure the reliability of the prediction sets. One approach is to incorporate a fallback mechanism that includes a broader range of potential responses, such as including an "I do not know" response in the aggregation set if the language model cannot generate a valid answer. This way, even if the primary models fail to provide accurate responses, the fallback option can help maintain the integrity of the prediction sets. Additionally, increasing the number of retrieved passages beyond the top-20 used in the experiments can enhance the chances of including relevant information in the prediction sets, even if the retriever's performance is subpar.

Q: What are the potential limitations of the semantic clustering techniques used in TRAQ, and how can they be improved?

The semantic clustering techniques used in TRAQ, such as Rouge score-based or BERT-based clustering, may have limitations in accurately capturing the semantic similarity between responses. One potential limitation is the reliance on specific similarity metrics, which may not always capture the nuances of semantic equivalence effectively. To improve these techniques, incorporating more advanced natural language processing models or similarity measures could enhance the clustering process. For example, leveraging transformer-based models like RoBERTa or XLNet for semantic similarity calculations could provide more nuanced and accurate clustering results. Additionally, exploring ensemble clustering methods that combine multiple similarity metrics or models could help mitigate the limitations of individual techniques and improve the overall clustering quality.

Q: How can the computational complexity of TRAQ be reduced to improve inference speed, especially for the retrieval phase?

To reduce the computational complexity of TRAQ and improve inference speed, especially during the retrieval phase, several optimization strategies can be implemented. One approach is to optimize the retrieval process by utilizing more efficient algorithms for passage retrieval, such as approximate nearest neighbor search techniques like Locality-Sensitive Hashing (LSH) or Faiss. These methods can significantly speed up the retrieval process by quickly identifying relevant passages based on similarity metrics. Additionally, implementing caching mechanisms to store and reuse previously retrieved passages can reduce redundant computations and expedite the retrieval process. Furthermore, optimizing the language model's inference process by leveraging techniques like batch processing, parallel computing, or model quantization can help accelerate response generation. By batching multiple inference requests together and utilizing parallel processing capabilities, the overall inference speed can be significantly improved. Additionally, model quantization techniques can reduce the computational resources required for inference without compromising the model's performance, further enhancing the overall efficiency of the TRAQ framework.

핵심 개념

TRAQ provides the first end-to-end statistical correctness guarantee for retrieval augmented question answering by leveraging conformal prediction.

초록

The paper proposes a novel framework called Trustworthy Retrieval Augmented Question Answering (TRAQ) that combines retrieval augmented generation (RAG) with conformal prediction to provide theoretical guarantees on question answering performance.

Key highlights:

TRAQ uses conformal prediction to construct prediction sets for the retriever and generator models separately, and then aggregates these sets to obtain an overall correctness guarantee.
TRAQ introduces a novel nonconformity measure that estimates the uncertainty at the semantic level, enabling it to work with black-box APIs where individual token probabilities are not available.
TRAQ leverages Bayesian optimization to minimize the average size of the generated prediction sets, improving the efficiency of the approach.
Extensive experiments demonstrate that TRAQ empirically satisfies the desired coverage guarantee while reducing the average prediction set size compared to an ablation by 16.2% on average.

요약 맞춤 설정

AI로 다시 쓰기

인용 생성

소스 번역

다른 언어로

마인드맵 생성

소스 콘텐츠 기반

소스 방문

arxiv.org

통계

Large language models (LLMs) frequently generate incorrect responses based on made-up facts, called hallucinations, in open-domain question answering tasks.
Retrieval augmented generation (RAG) is a promising strategy to avoid hallucinations, but it does not provide guarantees on its correctness.

인용구

"TRAQ uses conformal prediction, a statistical technique for constructing prediction sets that are guaranteed to contain the semantically correct response with high probability."
"TRAQ leverages Bayesian optimization to minimize the size of the constructed sets."

핵심 통찰 요약

TRAQ

by Shuo Li,Sang... 게시일 arxiv.org 04-09-2024

https://arxiv.org/pdf/2307.04642.pdf

더 깊은 질문

How can TRAQ be extended to handle cases where the underlying retriever and language model do not perform well?

In cases where the underlying retriever and language model do not perform well, TRAQ can be extended by implementing additional strategies to ensure the reliability of the prediction sets. One approach is to incorporate a fallback mechanism that includes a broader range of potential responses, such as including an "I do not know" response in the aggregation set if the language model cannot generate a valid answer. This way, even if the primary models fail to provide accurate responses, the fallback option can help maintain the integrity of the prediction sets. Additionally, increasing the number of retrieved passages beyond the top-20 used in the experiments can enhance the chances of including relevant information in the prediction sets, even if the retriever's performance is subpar.

What are the potential limitations of the semantic clustering techniques used in TRAQ, and how can they be improved?

The semantic clustering techniques used in TRAQ, such as Rouge score-based or BERT-based clustering, may have limitations in accurately capturing the semantic similarity between responses. One potential limitation is the reliance on specific similarity metrics, which may not always capture the nuances of semantic equivalence effectively. To improve these techniques, incorporating more advanced natural language processing models or similarity measures could enhance the clustering process. For example, leveraging transformer-based models like RoBERTa or XLNet for semantic similarity calculations could provide more nuanced and accurate clustering results. Additionally, exploring ensemble clustering methods that combine multiple similarity metrics or models could help mitigate the limitations of individual techniques and improve the overall clustering quality.

How can the computational complexity of TRAQ be reduced to improve inference speed, especially for the retrieval phase?

To reduce the computational complexity of TRAQ and improve inference speed, especially during the retrieval phase, several optimization strategies can be implemented. One approach is to optimize the retrieval process by utilizing more efficient algorithms for passage retrieval, such as approximate nearest neighbor search techniques like Locality-Sensitive Hashing (LSH) or Faiss. These methods can significantly speed up the retrieval process by quickly identifying relevant passages based on similarity metrics. Additionally, implementing caching mechanisms to store and reuse previously retrieved passages can reduce redundant computations and expedite the retrieval process.
Furthermore, optimizing the language model's inference process by leveraging techniques like batch processing, parallel computing, or model quantization can help accelerate response generation. By batching multiple inference requests together and utilizing parallel processing capabilities, the overall inference speed can be significantly improved. Additionally, model quantization techniques can reduce the computational resources required for inference without compromising the model's performance, further enhancing the overall efficiency of the TRAQ framework.