21 citations · 67 across the 13 of their papers we have counts for
13 papers
LLM2Vec-Gen: Generative Embeddings from Large Language Models
Parishad BehnamGhader, Vaibhav Adlakha, Fabian David Schmidt +3
Fine-tuning LLM-based text embedders via contrastive learning maps inputs and outputs into a new representational space, discarding the LLM's output semantics. We propose LLM2Vec-G…
LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs
Benno Krojer, Shravan Nayak, Oscar Mañas +4
Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM.…
Understanding the Influence of Synthetic Data for Text Embedders
Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy +2
Recent progress in developing general purpose text embedders has been driven by training on ever-growing corpora of synthetic LLM-generated data. Nonetheless, no publicly available…
BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation
Christos Tsirigotis, Vaibhav Adlakha, Joao Monteiro +2
Neural sentence embedding models for dense retrieval typically rely on binary relevance labels, treating query-document pairs as either relevant or irrelevant. However, real-world…
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanović, Arkil Patel, Vaibhav Adlakha +14
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creat…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach +3
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is only slowly adopting these models for t…
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu +2
Retriever-augmented instruction-following models are attractive alternatives to fine-tuned approaches for information-seeking tasks such as question answering (QA). By simply prepe…
Image Retrieval from Contextual Descriptions
Benno Krojer, Vaibhav Adlakha, Vibhav Vineet +3
The ability to integrate context, including perceptual and temporal cues, plays a pivotal role in grounding the meaning of a linguistic utterance. In order to measure to what exten…
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining
Andreas Madsen, Nicholas Meade, Vaibhav Adlakha +1
To explain NLP models a popular approach is to use importance measures, such as attention, which inform input tokens are important for making a prediction. However, an open questio…
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching
Vaibhav Adlakha, Shehzaad Dhuliawala, Kaheer Suleman +2
In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversati…
OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction
Keshav Kolluru, Vaibhav Adlakha, Samarth Aggarwal +2
A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs. This comes at a sign…
Regex Queries over Incomplete Knowledge Bases
Vaibhav Adlakha, Parth Shah, Srikanta Bedathur +1
We propose the novel task of answering regular expression queries (containing disjunction () and Kleene plus () operators) over incomplete KBs. The answer set of these que…