| Challenge: | Various long-distance dependencies have been investigated using neural language models. |
| Approach: | They examine whether large neural language models learn the long-distance dependency of British versus American spelling conventions . a large T5 language model does internalize consistency, but only with respect to observed lexical items . |
| Outcome: | The proposed model internalizes consistency with the training corpora, but only with respect to observed lexical items. |
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
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On the Calibration of Large Language Models and Alignment (2023.findings-emnlp)
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| Challenge: | Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure . |
| Approach: | They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training. |
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Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)
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| Challenge: | Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem. |
| Approach: | They propose to incorporate some of these measures into training objectives to enhance distributional robustness of LLMs. |
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Do Neural Language Models Show Preferences for Syntactic Formalisms? (2020.acl-main)
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| Challenge: | Recent work on interpretability of deep neural language models concludes that many properties of natural language syntax are encoded in their representational spaces. |
| Approach: | They propose to examine whether syntactic structure adheres to a surface-syntactical or deep syntaktic style of analysis. |
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Visual Grounding Helps Learn Word Meanings in Low-Data Regimes (2024.naacl-long)
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| Challenge: | Modern neural language models (LMs) require distinctly un-human-like ways to achieve these results. |
| Approach: | They train a diverse set of LM architectures with and without auxiliary visual supervision on datasets of varying scales. |
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Formality is Favored: Unraveling the Learning Preferences of Large Language Models on Data with Conflicting Knowledge (2024.emnlp-main)
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| Challenge: | Large language models have shown excellent performance on knowledge-intensive tasks, but pretraining data tends to contain misleading and conflicting information. |
| Approach: | They systematically analyze LLMs’ learning preferences for data with conflicting knowledge. |
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ABNIRML: Analyzing the Behavior of Neural IR Models (2022.tacl-1)
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| Challenge: | Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc ranking. |
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A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations. |
| Approach: | They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned. |
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Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
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How Reliable are Model Diagnostics? (2021.findings-acl)
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| Challenge: | Contemporary statistical models trade off interpretability and simplicity for powerful parameterizations and inductive biases, enabling impressive performance. |
| Approach: | They examine three recent models and find they are not yet reliable . they also formulate recommendations for practitioners and researchers . |
| Outcome: | The proposed models are not as reliable as previously assumed, the authors argue . their findings suggest that they are needed for improving models and training setups . |