Papers by Gonzalo Iglesias
Benchmarking Deflection and Hallucination in Large Vision-Language Models (2026.acl-long)
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| Challenge: | Existing benchmarks overlook conflicts between visual and textual evidence and the importance of generating deflections when incomplete knowledge is retrieved. |
| Approach: | They propose a dynamic curation pipeline that preserves benchmark difficulty over time . they propose 'vlm-DeflectionBench' benchmark to probe model behaviour under conflicting evidence . |
| Outcome: | The proposed benchmarks overlook conflicts between visual and textual evidence and are prone to obsolescence . the proposed benchmark is based on 2,775 samples spanning diverse retrieval settings . |
Neural Machine Translation Decoding with Terminology Constraints (N18-2)
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| Challenge: | Constrained neural machine translation systems can provide excellent quality but do not strictly enforce terminology. |
| Approach: | They propose a framework for constrained neural decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. |
| Outcome: | The proposed framework performs well on multiple translation tasks and motivates the need for constrained decoding with attentions to reduce misplacement and duplication when translating user constraints. |
An Inner Table Retriever for Robust Table Question Answering (2023.acl-long)
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| Challenge: | Table Question Answering (TableQA) is a task of answering NL user questions using factoid answers extracted from table content. |
| Approach: | They propose a method for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. |
| Outcome: | The proposed method can improve TableQA's accuracy with up to 1.3-4.8% and achieve state-of-the-art in two benchmarks. |
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment (N18-3)
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| Challenge: | LMBR techniques for NMT still yield better results than Transformers . but with NMT, real time decoding is challenging without GPUs and high-end GPUs are expensive. |
| Approach: | They propose a batched beam decoding algorithm for NMT with LMBR n-gram posteriors and an acceleration strategy for deployment to take advantage of the higher adequacy. |
| Outcome: | The proposed method outperforms the most recent results with Transformers in terms of speed and memory usage. |
LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering (2023.acl-short)
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| Challenge: | Recent open-domain TableQA pipelines use a combination of retriever and reader . a table can be very large and might contain heterogeneous information across rows/columns . |
| Approach: | They propose to combine a retriever-reader pipeline with a binary relevance token to train the retriever and reader. |
| Outcome: | The proposed approaches improve on two open-domain TableQA datasets. |