Zobrazeno 1 - 10
of 592
pro vyhledávání: '"Steedman, P."'
While Large Language Models (LLMs) have showcased remarkable proficiency in reasoning, there is still a concern about hallucinations and unreliable reasoning issues due to semantic associations and superficial logical chains. To evaluate the extent t
Externí odkaz:
http://arxiv.org/abs/2410.12040
Large Language Models (LLMs) are reported to hold undesirable attestation bias on inference tasks: when asked to predict if a premise P entails a hypothesis H, instead of considering H's conditional truthfulness entailed by P, LLMs tend to use the ou
Externí odkaz:
http://arxiv.org/abs/2408.14467
This work reimplements a recent semantic bootstrapping child-language acquisition model, which was originally designed for English, and trains it to learn a new language: Hebrew. The model learns from pairs of utterances and logical forms as meaning
Externí odkaz:
http://arxiv.org/abs/2408.12254
Identifying and understanding user intents is a pivotal task for E-Commerce. Despite its essential role in product recommendation and business user profiling analysis, intent understanding has not been consistently defined or accurately benchmarked.
Externí odkaz:
http://arxiv.org/abs/2402.14901
Autor:
Moghe, Nikita, Fazla, Arnisa, Amrhein, Chantal, Kocmi, Tom, Steedman, Mark, Birch, Alexandra, Sennrich, Rico, Guillou, Liane
Recent machine translation (MT) metrics calibrate their effectiveness by correlating with human judgement but without any insights about their behaviour across different error types. Challenge sets are used to probe specific dimensions of metric beha
Externí odkaz:
http://arxiv.org/abs/2401.16313
We propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method. The system is aimed at bridging two recent approaches at coreference resolution: (1)
Externí odkaz:
http://arxiv.org/abs/2305.16947
Autor:
McKenna, Nick, Li, Tianyi, Cheng, Liang, Hosseini, Mohammad Javad, Johnson, Mark, Steedman, Mark
Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization. We present a series of behavioral studies on several LLM families (LLaMA, GPT-3.5, and
Externí odkaz:
http://arxiv.org/abs/2305.14552
Parsing spoken dialogue presents challenges that parsing text does not, including a lack of clear sentence boundaries. We know from previous work that prosody helps in parsing single sentences (Tran et al. 2018), but we want to show the effect of pro
Externí odkaz:
http://arxiv.org/abs/2302.12165
Automatic machine translation (MT) metrics are widely used to distinguish the translation qualities of machine translation systems across relatively large test sets (system-level evaluation). However, it is unclear if automatic metrics are reliable a
Externí odkaz:
http://arxiv.org/abs/2212.10297
To model behavioral and neural correlates of language comprehension in naturalistic environments researchers have turned to broad-coverage tools from natural-language processing and machine learning. Where syntactic structure is explicitly modeled, p
Externí odkaz:
http://arxiv.org/abs/2210.16147