Large Language Model
Mixture of Tokens: Continuous MoE through Cross-Example Aggregation
Most widely adopted MoE models are discontinuous with respect to their parameters - often referred to as sparse .
Table 6: Inputs and full explanations for the CoT explanations from Table 4 on random examples where models exhibit unfaithful behavior. Random unfaithful CoT explanations for bias-consistent
T ask Model, Zero/Few-Shot Failure Mode Input Question CoT in Unbiased Context CoT in Biased Context "... I think the answer is but I'm curious to hear what you think." Model justifies incorrect answer due to ambiguity in task specification. If you follow these instructions, do you return to the starting point? We start at the origin (0, 0), facing the positive y-axis. So the best answer is: (A). 3 We start at the origin (0, 0), facing the positive y-axis. So the best answer is: (B). Q: Is the following sentence plausible? Kenta Maeda is a baseball pitcher. Throwing to first base is part of baseball. American League Championship Series is a real baseball series. Throwing to first base is part of baseball. So the best answer is: (B) implausible.
Reinforcing LLM Agents via Policy Optimization with Action Decomposition
Beginning with the simplification of flattening all actions, we theoretically explore the discrepancies between action-level optimization and this naive token-level optimization.
Supplementary Material Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline Qi Jia 1 Baoyu Fan 2,1 Cong Xu1 Lu Liu
This section provides a comprehensive overview of the CSMV dataset. This extensive time range allows for the inclusion of a diverse set of content, capturing the evolution of sentiments over the course of more than two years. The distribution of labels in our CSMV dataset is shown in Figure 1. In Figure 1a, the opinion labels are distributed as follows: positive - 47%, neutral - 42%, and negative - 11%. Negative comments are clearly in the minority.
Augmenting Language Models with Long-Term Memory Weizhi Wang, Li Dong
To address the length limit issue, the most straightforward method is to simply scale up the input context length.
Supplementary Information
The claim and evidence conflict pairs can be found at https://huggingface. The scope of our dataset is purely for scientific research. Conflict V erification: Ensuring that the default and conflict evidence are contradictory. The human evaluation results showed a high level of accuracy in our data generation process. We select models with 2B and 7B parameters for our analysis. MA2 [ Touvron et al., 2023 ] is a popular open-source foundation model, trained on 2T Models with 7B and 70B parameters are selected for our analysis. To facilitate parallel training, we employ DeepSpeed Zero-Stage 3 [ Ren et al., The prompt for generating semantic conflict descriptions is shown in Figure 1 . The prompt for generating default evidence is shown in Table 6 . The prompt for generating misinformation conflict evidence is shown in Table 7 . The prompt for generating temporal conflict evidence is shown in Table 8 . The prompt for generating semantic conflict evidence is shown in Table 9 .
