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Experience Grounds Language

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arxiv 2004.10151 v3 pith:UE2ETW6C submitted 2020-04-21 cs.CL cs.AIcs.LG

Experience Grounds Language

Yonatan Bisk , Ari Holtzman , Jesse Thomason , Jacob Andreas , Yoshua Bengio , Joyce Chai , Mirella Lapata , Angeliki Lazaridou
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Jonathan May Aleksandr Nisnevich Nicolas Pinto Joseph Turian
This is my paper
classification cs.CL cs.AIcs.LG
keywords languageexperiencecommunicationphysicalprocessingresearchsharedsocial
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Measuring Massive Multitask Language Understanding

    cs.CY 2020-09 accept novelty 8.0

    Introduces the MMLU benchmark of 57 tasks and shows that current models, including GPT-3, achieve low accuracy far below expert level across academic and professional domains.

  2. Language Models are Few-Shot Learners

    cs.CL 2020-05 accept novelty 8.0

    GPT-3 shows that scaling an autoregressive language model to 175 billion parameters enables strong few-shot performance across diverse NLP tasks via in-context prompting without fine-tuning.

  3. Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt

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    Larger LLMs reproduce constructional productivity via entrenchment in coercion cases with nonce words but fail to use statistical preemption to avoid overgeneralizing semantically plausible but unobserved patterns.

  4. When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models

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    Quantized (INT8/INT4) LLMs can outperform FP16 in later-task forward accuracy and retention during continual learning, though single-seed runs leave the effect unquantified.