6 citations · 14 across the 16 of their papers we have counts for
16 papers · 1 filter
Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models
Francesco Maria Molfese, Momchil Hardalov, Rexhina Blloshmi +2
With context windows of millions of tokens, Long-Context Language Models (LCLMs) can encode entire document collections, offering a strong alternative to conventional retrieval-aug…
RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation
Andrei C. Coman, Ionut-Teodor Sorodoc, Leonardo F. R. Ribeiro +3
Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfuln…
GaRAGe: A Benchmark with Grounding Annotations for RAG Evaluation
Ionut-Teodor Sorodoc, Leonardo F. R. Ribeiro, Rexhina Blloshmi +2
We present GaRAGe, a large RAG benchmark with human-curated long-form answers and annotations of each grounding passage, allowing a fine-grained evaluation of whether LLMs can iden…
Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision
Philipp Christmann, Svitlana Vakulenko, Ionut Teodor Sorodoc +2
Long-form question answering (LFQA) aims at generating in-depth answers to end-user questions, providing relevant information beyond the direct answer. However, existing retrievers…
Strong and Efficient Baselines for Open Domain Conversational Question Answering
Andrei C. Coman, Gianni Barlacchi, Adrià de Gispert
Unlike the Open Domain Question Answering (ODQA) setting, the conversational (ODConvQA) domain has received limited attention when it comes to reevaluating baselines for both effic…
xPQA: Cross-Lingual Product Question Answering across 12 Languages
Xiaoyu Shen, Akari Asai, Bill Byrne +1
Product Question Answering (PQA) systems are key in e-commerce applications to provide responses to customers' questions as they shop for products. While existing work on PQA focus…
Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey
Xiaoyu Shen, Svitlana Vakulenko, Marco del Tredici +3
Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs) achieved significant advances and have become a key component for modern open-domain question-a…
From Rewriting to Remembering: Common Ground for Conversational QA Models
Marco Del Tredici, Xiaoyu Shen, Gianni Barlacchi +2
In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract releva…
Cued@wmt19:ewc&lms
Felix Stahlberg, Danielle Saunders, Adria de Gispert +1
Two techniques provide the fabric of the Cambridge University Engineering Department's (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and differe…
Domain Adaptive Inference for Neural Machine Translation
Danielle Saunders, Felix Stahlberg, Adria de Gispert +1
We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing…
The University of Cambridge's Machine Translation Systems for WMT18
Felix Stahlberg, Adria de Gispert, Bill Byrne
The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation. We compare recurrent, convolutional, and self…
Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT
Danielle Saunders, Felix Stahlberg, Adria de Gispert +1
We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We…
Neural Machine Translation Decoding with Terminology Constraints
Eva Hasler, Adrià De Gispert, Gonzalo Iglesias +1
Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constra…
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
Gonzalo Iglesias, William Tambellini, Adrià De Gispert +2
We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with…
A Comparison of Neural Models for Word Ordering
Eva Hasler, Felix Stahlberg, Marcus Tomalin +2
We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the m…
Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
Daniel Beck, Adrià de Gispert, Gonzalo Iglesias +2
We address the problem of automatically finding the parameters of a statistical machine translation system that maximize BLEU scores while ensuring that decoding speed exceeds a mi…