Fetching the paper…

Reading the bibliography…

2024

Reinforcement Learning for Optimizing RAG for Domain Chatbots

Kulkarni, Mandar, Tangarajan, Praveen, Kim, Kyung et al.

Original
PDF
Post on X

Understand

With the advent of Large Language Models (LLM), conversational assistants have become prevalent for domain use cases.

LLMs acquire the ability to contextual question answering through training, and Retrieval Augmented Generation (RAG) further enables the bot to answer domain-specific questions.
This paper describes a RAG-based approach for building a chatbot that answers user's queries using Frequently Asked Questions (FAQ) data.
We train an in-house retrieval embedding model using infoNCE loss, and experimental results demonstrate that the in-house model works significantly better than the well-known general-purpose public embedding model, both in terms of retrieval accuracy and Out-of-Domain (OOD) query detection.

Reinforcement Learning for Optimizing RAG for Domain Chatbots · Around

Built on

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Original
Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; tau Yih, W.; Rocktäschel, T.; Riedel, S.; and Kiela, D. 2021 · 2005
Earlier work this paper cites.
Understanding the impact of entropy on policy optimization
Original
Ahmed, Z.; Roux, N. L.; Norouzi, M.; and Schuurmans, D. 2019 · 2019
Earlier work this paper cites.
Representation Learning with Contrastive Predictive Coding
Original
van den Oord, A.; Li, Y.; and Vinyals, O. 2019 · 2019
Earlier work this paper cites.
Self-supervised Pretraining of Visual Features in the Wild
Original
Goyal, P.; Caron, M.; Lefaudeux, B.; Xu, M.; Wang, P.; Pai, V.; Singh, M.; Liptchinsky, V.; Misra, I.; Joulin, A.; and Bojanowski, P. 2021 · 2021
Earlier work this paper cites.

Similar

A survey on retrieval-augmented text generation
Original
Li, H.; Su, Y.; Cai, D.; Wang, Y.; and Liu, L. 2022 · 2022
Cited alongside, same era.
Text Embeddings by Weakly-Supervised Contrastive Pre-training
Original
Wang, L.; Yang, N.; Huang, X.; Jiao, B.; Yang, L.; Jiang, D.; Majumder, R.; and Wei, F. 2022 · 2022
Cited alongside, same era.
Augmented Embeddings for Custom Retrievals
Original
2023 · 2023
Cited alongside, same era.
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Original
Asai, A.; Wu, Z.; Wang, Y.; Sil, A.; and Hajishirzi, H. 2023 · 2023
Cited alongside, same era.
Lost in the Middle: How Language Models Use Long Contexts

Then

RRAML: Reinforced Retrieval Augmented Machine Learning
Bacciu, A.; Cocunasu, F.; Siciliano, F.; Silvestri, F.; Tonellotto, N.; and Trappolini, G. 2023 · 2023
Later among the works it cites.
Is GPT-4 a reliable rater? Evaluating Consistency in GPT-4 Text Ratings
Original
Hackl, V.; Müller, A. E.; Granitzer, M.; and Sailer, M. 2023 · 2023
Later among the works it cites.
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
Original
Shao, Z.; Gong, Y.; Shen, Y.; Huang, M.; Duan, N.; and Chen, W. 2023 · 2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Liu, N. F.; Lin, K.; Hewitt, J.; Paranjape, A.; Bevilacqua, M.; Petroni, F.; and Liang, P. 2023a

Cited in the paper.

G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Liu, Y.; Iter, D.; Xu, Y.; Wang, S.; Xu, R.; and Zhu, C. 2023b

Cited in the paper.