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Found 57 papers, 50 papers with code
RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts
Retrieval-Augmented Generation (RAG) enhances the performance of Large Language Models (LLMs) by incorporating external knowledge.
Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
This paper introduces Multi-Modal Retrieval-Augmented Generation (M^2RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models (MLLMs) in leveraging knowledge from multi-modal retrieval documents.
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference Optimization
Tabular data contains rich structural semantics and plays a crucial role in organizing and manipulating information.
LLM-QE: Improving Query Expansion by Aligning Large Language Models with Ranking Preferences
Query expansion plays a crucial role in information retrieval, which aims to bridge the semantic gap between queries and documents to improve matching performance.
PIP-KAG: Mitigating Knowledge Conflicts in Knowledge-Augmented Generation via Parametric Pruning
Knowledge-Augmented Generation (KAG) has shown great promise in updating the internal memory of Large Language Models (LLMs) by integrating external knowledge.
Learning More Effective Representations for Dense Retrieval through Deliberate Thinking Before Search
Recent dense retrievers usually thrive on the emergency capabilities of Large Language Models (LLMs), using them to encode queries and documents into an embedding space for retrieval.
PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
Typical RAG approaches split the text database into chunks, organizing them in a flat structure for efficient searches.
KBAlign: Efficient Self Adaptation on Specific Knowledge Bases
Humans can utilize techniques to quickly acquire knowledge from specific materials in advance, such as creating self-assessment questions, enabling us to achieving related tasks more efficiently.
Building A Coding Assistant via the Retrieval-Augmented Language Model
Specifically, it consists of a code structure aware retriever (CONAN-R) and a dual-view code representation-based retrieval-augmented generation model (CONAN-G).
RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
Our experiments on various knowledge-intensive tasks demonstrate that DDR significantly outperforms the SFT method, particularly for LLMs with smaller-scale parameters that depend more on the retrieved knowledge.
VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM.
Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) mitigates issues of the factual errors and hallucinated outputs generated by Large Language Models (LLMs) in open-domain question-answering tasks (OpenQA) via introducing external knowledge.
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs
Besides, the construction method can also be applied to civil cases and achieve promising results.
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis
In this paper, we introduce DEBUGEVAL, a comprehensive benchmark for evaluating the debugging abilities of LLMs by emulating the multi-stage human debugging process.
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge.
PersLLM: A Personified Training Approach for Large Language Models
Large language models exhibit aspects of human-level intelligence that catalyze their application as human-like agents in domains such as social simulations, human-machine interactions, and collaborative multi-agent systems.
Node-Time Conditional Prompt Learning In Dynamic Graphs
To bridge the gap, prompt-based learning has gained traction on graphs, but most existing efforts focus on static graphs, neglecting the evolution of dynamic graphs.
Multi-Evidence based Fact Verification via A Confidential Graph Neural Network
Nevertheless, the noisy nodes usually propagate their semantics via the edges of the reasoning graph, which misleads the semantic representations of other nodes and amplifies the noise signals.
Advancing LLM Reasoning Generalists with Preference Trees
We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning.
Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression
It finetunes the compression plugin module and uses the representations of gist tokens to emulate the raw prompts in the vanilla language model.
Cleaner Pretraining Corpus Curation with Neural Web Scraping
The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans.
From Text to CQL: Bridging Natural Language and Corpus Search Engine
This paper presents the first text-to-CQL task that aims to automate the translation of natural language into CQL.
OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models
We evaluate several representative multilingual LLMs on the proposed OMGEval, which we believe will provide a valuable reference for the community to further understand and improve the multilingual capability of LLMs.
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to leverage external knowledge, enhancing their performance on knowledge-intensive tasks.
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns.
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset
Different from previous works that simply translate English instructions, we consider both the language-specific and language-agnostic abilities of LLMs.
LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning
It proposes a dual-view legal clue reasoning mechanism, which derives from two reasoning chains of judges: 1) Law Case Reasoning, which makes legal judgments according to the judgment experiences learned from analogy/confusing legal cases; 2) Legal Ground Reasoning, which lies in matching the legal clues between criminal cases and legal decisions.
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs.
INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair
INTERVENOR prompts Large Language Models (LLMs) to play distinct roles during the code repair process, functioning as both a Code Learner and a Code Teacher.
Ranked #29 on
Code Generation
on MBPP
Modeling User Viewing Flow Using Large Language Models for Article Recommendation
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles.
Enhancing Dense Retrievers' Robustness with Group-level Reweighting
In this paper, we introduce WebDRO, an efficient approach for clustering the web graph data and optimizing group weights to enhance the robustness of dense retrieval models.
MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
To facilitate the multi-modal retrieval tasks, we build the ClueWeb22-MM dataset based on the ClueWeb22 dataset, which regards anchor texts as queries, and extracts the related text and image documents from anchor-linked web pages.
Toolink: Linking Toolkit Creation and Using through Chain-of-Solving on Open-Source Model
We first validate the efficacy of Toolink in harnessing the model's creativity and CoS ability on ChatGPT.
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems.
MCTS: A Multi-Reference Chinese Text Simplification Dataset
We hope to build a basic understanding of Chinese text simplification through the foundational work and provide references for future research.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining.
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step.
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data.
Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval
To learn a unified embedding space for multi-modal retrieval, UniVL-DR proposes two techniques: 1) Universal embedding optimization strategy, which contrastively optimizes the embedding space using the modality-balanced hard negatives; 2) Image verbalization method, which bridges the modality gap between images and texts in the raw data space.
Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder
These embeddings need to be high-dimensional to fit training signals and guarantee the retrieval effectiveness of dense retrievers.
Ranked #1 on
Information Retrieval
on MS MARCO
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
YACLC: A Chinese Learner Corpus with Multidimensional Annotation
This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction.
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling
In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored.
More Robust Dense Retrieval with Contrastive Dual Learning
With contrastive learning, the dual training object of DANCE learns more tailored representations for queries and documents to keep the embedding space smooth and uniform, thriving on the ranking performance of DANCE on the MS MARCO document retrieval task.
Few-Shot Conversational Dense Retrieval
In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products.
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills.
Ranked #1 on
Grammatical Error Detection
on FCE
OpenMatch: An Open Source Library for Neu-IR Research
OpenMatch is a Python-based library that serves for Neural Information Retrieval (Neu-IR) research.
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains.
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks.
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling
With the epidemic of COVID-19, verifying the scientifically false online information, such as fake news and maliciously fabricated statements, has become crucial.
Toward Cross-Lingual Definition Generation for Language Learners
We demonstrate the effectiveness of this approach on zero-shot definition generation.
Capturing Global Informativeness in Open Domain Keyphrase Extraction
Open-domain KeyPhrase Extraction (KPE) aims to extract keyphrases from documents without domain or quality restrictions, e. g., web pages with variant domains and qualities.
Selective Weak Supervision for Neural Information Retrieval
This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available.
Fine-grained Fact Verification with Kernel Graph Attention Network
Fact Verification requires fine-grained natural language inference capability that finds subtle clues to identify the syntactical and semantically correct but not well-supported claims.
Ranked #5 on
Fact Verification
on FEVER
Explore Entity Embedding Effectiveness in Entity Retrieval
Entity embedding learns lots of semantic information from the knowledge graph and represents entities with a low-dimensional representation, which provides an opportunity to establish interactions between query related entities and candidate entities for entity retrieval.
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems.
