Search Results for author:
Found 26 papers, 8 papers with code
Found in Translation: Measuring Multilingual LLM Consistency as Simple as Translate then Evaluate
These findings necessitate cross-lingual evaluations that are consistent along multiple dimensions.
SLOT: Structuring the Output of Large Language Models
Structured outputs are essential for large language models (LLMs) in critical applications like agents and information extraction.
CSPLADE: Learned Sparse Retrieval with Causal Language Models
In this work, we identify two challenges in training large language models (LLM) for LSR: (1) training instability during the early stage of contrastive training; (2) suboptimal performance due to pre-trained LLM's unidirectional attention.
A Systematic Survey of Automatic Prompt Optimization Techniques
Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks.
A Survey of Model Architectures in Information Retrieval
This survey examines the evolution of model architectures in information retrieval (IR), focusing on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation
Large Language Models (LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information.
State Space Models are Strong Text Rerankers
Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures.
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression
To this end, we investigate the impact of model compression along four dimensions: (1) degeneration harm, i. e., bias and toxicity in generation; (2) representational harm, i. e., biases in discriminative tasks; (3) dialect bias; and(4) language modeling and downstream task performance.
RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers
In this work, we examine \mamba's efficacy through the lens of a classical IR task -- document ranking.
In-Context Example Ordering Guided by Label Distributions
Inspired by the idea of learning from label proportions, we propose two principles for in-context example ordering guided by model's probability predictions.
Multi-dimensional Evaluation of Empathetic Dialog Responses
Prior efforts to measure conversational empathy mostly focus on expressed communicative intents -- that is, the way empathy is expressed.
Context-aware Decoding Reduces Hallucination in Query-focused Summarization
Query-focused summarization (QFS) aims to provide a summary of a single document/multi documents that can satisfy the information needs of a given query.
FARA: Future-aware Ranking Algorithm for Fairness Optimization
However, we find that most existing fair ranking methods adopt greedy algorithms that only optimize rankings for the next immediate session or request.
A Lightweight Constrained Generation Alternative for Query-focused Summarization
Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstractive snippet generation.
An In-depth Investigation of User Response Simulation for Conversational Search
Our goal is to supplement existing work with an insightful hand-analysis of unsolved challenges by the baseline and propose our solutions.
Reward-free Policy Imitation Learning for Conversational Search
In this paper, we propose a reward-free conversation policy imitation learning framework, which can train a conversation policy without annotated conversation data or manually designed rewards.
Counterfactual Editing for Search Result Explanation
Next, we formulate a suite of desiderata for counterfactual explanation in SeRE task and corresponding automatic metrics.
Marginal-Certainty-aware Fair Ranking Algorithm
In MCFair, we first develop a ranking objective that includes uncertainty, fairness, and user utility.
1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track
The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy.
Reinforcement Learning to Rank with Coarse-grained Labels
The results on simulated coarse-grained labeled dataset show that while using coarse-grained labels to train an RL model for LTR tasks still can not outperform traditional approaches using fine-grained labels, it still achieve somewhat promising results and is potentially helpful for future research in LTR.
Learning to Rank Rationales for Explainable Recommendation
Seeing this gap, we propose a model named Semantic-Enhanced Bayesian Personalized Explanation Ranking (SE-BPER) to effectively combine the interaction information and semantic information.
Vertical Allocation-based Fair Exposure Amortizing in Ranking
Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services.
Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method.
A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
User and item reviews are valuable for the construction of recommender systems.
E-commerce Recommendation with Weighted Expected Utility
In this paper, we interpret purchase utility as the satisfaction level a consumer gets from a product and propose a recommendation framework using EU to model consumers' behavioral patterns.
Using Sampling Strategy to Assist Consensus Sequence Analysis
Consensus Sequences of event logs are often used in process mining to quickly grasp the core sequence of events to be performed in a process, or to represent the backbone of the process for doing other analyses.
