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Found 20 papers, 14 papers with code
Self-Refine: Iterative Refinement with Self-Feedback
Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.
PAL: Program-aided Language Models
Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem.
Ranked #17 on
Arithmetic Reasoning
on GSM8K
Active Retrieval Augmented Generation
In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation.
Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval
In this paper, we present Tevatron, a dense retrieval toolkit optimized for efficiency, flexibility, and code simplicity.
Precise Zero-Shot Dense Retrieval without Relevance Labels
Given a query, HyDE first zero-shot instructs an instruction-following language model (e. g. InstructGPT) to generate a hypothetical document.
Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup
Contrastive learning has been applied successfully to learn vector representations of text.
Condenser: a Pre-training Architecture for Dense Retrieval
Pre-trained Transformer language models (LM) have become go-to text representation encoders.
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Recent research demonstrates the effectiveness of using fine-tuned language models~(LM) for dense retrieval.
Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval.
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index.
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers.
Ranked #1 on
Passage Retrieval
on Natural Questions
RARR: Researching and Revising What Language Models Say, Using Language Models
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
Using this data, we compare various information retrieval algorithms on our test set and present a superior bi-encoder retriever for text-based dataset recommendation.
Long Document Re-ranking with Modular Re-ranker
In this paper, we propose instead to model full query-to-document interaction, leveraging the attention operation and modular Transformer re-ranker framework.
Modularized Transfomer-based Ranking Framework
Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval.
Complementing Lexical Retrieval with Semantic Residual Embedding
This paper presents CLEAR, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching signals from a neural embedding matching model.
Understanding BERT Rankers Under Distillation
Deep language models such as BERT pre-trained on large corpus have given a huge performance boost to the state-of-the-art information retrieval ranking systems.
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation
To improve the performance of Neural Machine Translation~(NMT) for low-resource languages~(LRL), one effective strategy is to leverage parallel data from a related high-resource language~(HRL).
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries.
In-Context Principle Learning from Mistakes
We evaluate LEAP on a wide range of benchmarks, including multi-hop question answering (Hotpot QA), textual QA (DROP), Big-Bench Hard reasoning, and math problems (GSM8K and MATH); in all these benchmarks, LEAP improves the strongest available LLMs such as GPT-3. 5-turbo, GPT-4, GPT-4 turbo and Claude-2. 1.
