25 citations · 56 across the 7 of their papers we have counts for
10 papers
The CRINGE Loss: Learning what language not to model
Leonard Adolphs, Tianyu Gao, Jing Xu +3
Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples. Growing evidence shows that even w…
Decoding a Neural Retriever's Latent Space for Query Suggestion
Leonard Adolphs, Michelle Chen Huebscher, Christian Buck +4
Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice. However, neural systems lack the interpretability of bag-of-…
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion
Kurt Shuster, Mojtaba Komeili, Leonard Adolphs +3
Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2021) in combination with retrieval (Adolphs et al., 2021).…
Calibration of Machine Reading Systems at Scale
Shehzaad Dhuliawala, Leonard Adolphs, Rajarshi Das +1
In typical machine learning systems, an estimate of the probability of the prediction is used to assess the system's confidence in the prediction. This confidence measure is usuall…
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Leonard Adolphs, Kurt Shuster, Jack Urbanek +2
Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a difficul…
Boosting Search Engines with Interactive Agents
Leonard Adolphs, Benjamin Boerschinger, Christian Buck +8
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses mach…