An unsupervised technique extracts latent yes-no knowledge from language model activations by locating a direction that satisfies logical consistency properties, outperforming zero-shot accuracy by 4% on average across models and datasets.
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BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
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In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingly, continues to be very beneficial even when starting from massive pre-trained language models such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on our train set. It achieves 80.4% accuracy compared to 90% accuracy of human annotators (and 62% majority-baseline), leaving a significant gap for future work.
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- abstract In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingl
- dataset els with different architectures, including LLaMA-2 [62], Mistral [28], and Mixtral [29], where Mixtral is a Mixture-of-Experts (MoE) model. Model accuracy is measured on multiple datasets, including the Massive Multitask Language Understanding (MMLU) [25] in the five-shot setting and zero-shot Commonsense QA benchmarks such as WinoGrande [58], PIQA [12], HellaSwag [68], ARC [16], BoolQ [15] and OBQA [45]. All evaluations are conducted using the Language Model Evaluation Harness [21]. 4.2 In-Net
- dataset MultiRC dataset encompasses around 6, 000 multi- sentence questions gathered from over 800 paragraphs. On average, each question offers about two valid answer alternatives out of a total of five. B. Datasets for Emergent: ICL, reasoning (CoT), instruction following This section centers on the benchmarks and datasets em- ployed to evaluate the emergent abilities of LLMs. • GSM8K [190] is designed to evaluate the model's ability for multi-step mathematical reasoning. GSM8K includes 8.5K linguistic
- background the chunk size C is the small fixed constant (More detail in the § D with Algorithm 1). Further, the chunk form of bt and theγ t,i are computed as following Eq.(10) and (11), b[t] = exp(µlog [t] +c log [t] )∈R C,(10) Γ[t] = exp(eAlog [t] )⊙ 1−exp(S log [t] ) ∈R C×C ,(11) where the chunk matrix eAlog [t] ,S log [t] ∈R C×C is computed, (eAlog [t] )ij = ( ¯αlog [t] +c log [t] )i −( ¯µlog [t] )j fori≥j,(12) (Slog [t] )ij = (clog [t] )j−1 −(c log [t] )i fori≥j.(13) These lower triangular matrices
- background with the hope of stimulating further study of test-time behavior of language models. 3.9.1 Arithmetic To test GPT-3's ability to perform simple arithmetic operations without task-specific training, we developed a small battery of 10 tests that involve asking GPT-3 a simple arithmetic problem in natural language: • 2 digit addition (2D+) - The model is asked to add two integers sampled uniformly from [0, 100), phrased in the form of a question, e.g. "Q: What is 48 plus 76? A: 124." • 2 digit subtr
- background Moreover, to ensure that the whole Mamba block output matches that of Hedgehog at initialization, we also set the parameters of the gate branch and the convolution so that they reduce to the identity operator. Additional details can be found in App. B. Attention scores normalization With the substitution in (7), the SSM mixer outputs Yϕ := ϕMLP(Q)ϕMLP(K)T V . (8) However, the Attention scores in this formula come in an un-normalized fashion. For the Attention scores formulation to more closely
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Deep Delta Learning replaces additive residual updates with a gated delta-rule that selectively overwrites residual content along learned directions, improving language modeling quality over standard ResNet-style accumulation.
MIDUS replaces duplicated FFN branches in depth up-scaling with head-wise memory layers using product-key retrieval and HIVE to deliver lightweight, head-conditioned residual capacity.
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UQ4CT integrates functional-level uncertainty calibration into mixture-of-experts LoRA fine-tuning via a dedicated loss, cutting expected calibration error by over 25% on multiple-choice and generative QA tasks.
SpinQuant learns optimal rotations to enable accurate 4-bit quantization of LLM weights, activations, and KV cache, reducing the zero-shot gap to full precision to 2.9 points on LLaMA-2 7B.
BitNet b1.58 shows that ternary 1.58-bit LLMs can match full-precision performance at substantially lower inference cost.
Massive activations are constant large values in LLMs that function as indispensable bias terms and concentrate attention probabilities on specific tokens.
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SparseForge achieves 57.27% zero-shot accuracy on LLaMA-2-7B at 2:4 sparsity using only 5B retraining tokens, beating the dense baseline and nearly matching a 40B-token SOTA method.
MoLS scales Adam updates using module-level SNR estimates to correct gradient noise imbalance and improve LLM training convergence and generalization.
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