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Manaal Faruqui

6 accepted papers

2025

Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation

NAACL 2025long

Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to understand user queries, retrieve relevant information…

Cited by 15SourcePDFScholar
2024

AutoMix: Automatically Mixing Language Models

NeurIPS 2024poster

Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present AutoMi…

2024

Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation

EMNLP 2024main

As large language models (LLMs) evolve, evaluating their output reliably becomes increasingly difficult due to the high cost of human evaluation. To address this, we introduce FLAMe, a family of Foundational Large Autorater Models. FLAMe is trained on a diverse set of over 100 quality assessment tas…

Cited by 41SourcePDFScholar
2021

TIMEDIAL: Temporal Commonsense Reasoning in Dialog

ACL 2021long

Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialo…

2018

(Almost) Zero-Shot Cross-Lingual Spoken Language Understanding

ICASSP 2018accepted

Spoken language understanding (SLU) is a component of goal-oriented dialogue systems that aims to interpret user's natural language queries in system's semantic representation format. While current state-of-the-art SLU approaches achieve high performance for English domains, the same is not true for…

Cited by 0SourceScholar
2015

Learning Word Representations with Hierarchical Sparse Coding

ICML 2015poster

We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making i…

Cited by 75SourcePDFScholar