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Qasa_annotated.tsv

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-Qasa_id	Qasa_paper_id	Question	Composition	Cited_content	Twisted_composition	How_to_twist	Twist reason	Modified_composition
+Qasa_id	Qasa_paper_id	Question	Composition	Cited_content	Twisted_composition	Twist_category	Twist_reason	Modified_composition
 0	paper_1	How do these automated metrics for human preferences differ and what factors do they consider when predicting human preferences?	The automated metrics that are mentioned while discussing related work are BERTScore (Zhang et al. , 2019), BLEURT (Sellam et al. , 2020), and Ouyang et al (2022).  More information on these automated metrics, including the differences between them, can probably be gleaned by reading these cited works.  The current paper does not contain any additional information about these related automated metrics.	The ultimate aim of language technology is to interact with humans.However, most language models are trained without direct signals of human preference,with supervised target strings serving as (a sometimes crude) proxy.One option to incorporate user feedback is via human-in-the-loop, i.e., a user would be expected to provide feedback for each sample online as the model trains, but this degree of dense supervision is often prohibitive and inefficient.Automated metrics offer a promising compromise: learned models of human preference like BERTScore (Zhang et al., 2019), BLEURT (Sellam et al., 2020), summarization preferences (Wu et al., 2021) have significantly improved correlation with human judgment compared to earlier metrics (BLEU, METEOR, etc.), and are cheap to evaluate. But — these functions are usually not per-token differentiable: like humans, metrics can only offer quality estimates for full generations.Reinforcement Learning (RL) offers a natural path forward for optimizing non-differentiable, scalar objectives for LM-based generation when it is cast as a sequential decision-making problem. However, Goodhart’s Law222Strathern (1997) paraphrases: When a measure becomes a target, it ceases to be a good measure.looms: particularly in the case of imperfect metrics that use neural networks, it is easy to find nonsense samples that achieve high-quality estimates. Recent works have shown promising results in aligning LMs to human preferences via RL by constraining preference-based rewards to incorporate notions of fluency (Wu et al., 2021; Ouyang et al., 2022) but progress in this line of work is heavily hindered by a lack of open-source benchmarks and algorithmic implementations—resulting in perception that RL is a challenging paradigm for NLP (Choshen et al., 2020; Kreutzer et al., 2021).	None			
 1	paper_1	What does non-differentiable mean here? If the problem with previous metrics is that they are not per-token differentiable then why are they looking for a way to optimize non-differentiable objectives?	A formal definition of non-differentiability has not been provided by the authors.  However, authors in this context use this idea of differentiability to explain that most automated metrics cannot provide quality estimates after a language model generates one token or a parietal output - similar to humans, they can provide quality estimates only after a language model generates a full sequence.  This non-differentiability is pointed out as a problem by the authors for the new metrics (i.  things like BLUERT or BERTScore) - it is unclear from this paper alone if previous vanilla metrics (i.  BLEU) were per token differentiable or not.  The authors are looking for ways to use these non-differentiable scores in RL frameworks, potentially as a reward function.	The ultimate aim of language technology is to interact with humans.However, most language models are trained without direct signals of human preference,with supervised target strings serving as (a sometimes crude) proxy.One option to incorporate user feedback is via human-in-the-loop, i.e., a user would be expected to provide feedback for each sample online as the model trains, but this degree of dense supervision is often prohibitive and inefficient.Automated metrics offer a promising compromise: learned models of human preference like BERTScore (Zhang et al., 2019), BLEURT (Sellam et al., 2020), summarization preferences (Wu et al., 2021) have significantly improved correlation with human judgment compared to earlier metrics (BLEU, METEOR, etc.), and are cheap to evaluate. But — these functions are usually not per-token differentiable: like humans, metrics can only offer quality estimates for full generations.Reinforcement Learning (RL) offers a natural path forward for optimizing non-differentiable, scalar objectives for LM-based generation when it is cast as a sequential decision-making problem. However, Goodhart’s Law222Strathern (1997) paraphrases: When a measure becomes a target, it ceases to be a good measure.looms: particularly in the case of imperfect metrics that use neural networks, it is easy to find nonsense samples that achieve high-quality estimates. Recent works have shown promising results in aligning LMs to human preferences via RL by constraining preference-based rewards to incorporate notions of fluency (Wu et al., 2021; Ouyang et al., 2022) but progress in this line of work is heavily hindered by a lack of open-source benchmarks and algorithmic implementations—resulting in perception that RL is a challenging paradigm for NLP (Choshen et al., 2020; Kreutzer et al., 2021).	A formal definition of non-differentiability has not been provided by the authors.  However, authors in this context use this idea of differentiability to explain that most automated metrics can provide quality estimates after a language model generates one token or a parietal output - similar to humans, they can provide quality estimates only after a language model generates a full sequence.  This non-differentiability is pointed out as a problem by the authors for the new metrics (i.  things like BLUERT or BERTScore) - it is unclear from this paper alone if previous vanilla metrics (i.  BLEU) were per token differentiable or not.  The authors are looking for ways to use these non-differentiable scores in RL frameworks, potentially as a reward function.	Opposite		
 2	paper_1	Why is the action space of language modeling particularly large? Is it because of the vocab size? But then, moving in the real world also has a huge action space (degrees of movement).	The action space for language modeling is equal to the vocabulary set of language models.  Since the vocabularies are very large (i.  tens of thousands of possible tokens), the action space is also very large.  In general, locomotion in the real world can be condensed to three quantities - moving across X, Y or Z axes, or a linear combination thereof.  The authors mention that typical RL problems have an action space that is an order of magnitude smaller, but do not specifically explain how the action spaces for typical problems is modeled or constructed.	Language generation action spaces are orders of magnitude larger than what most discrete action space RL algorithms are designed for (Ranzato et al., 2015; Ammanabrolu, 2021), e.g., GPT-2/3 and T5 have a vocabulary size of 50K and 32K respectively.We hypothesize that the size of the action space is a core cause of instability when training LMs with existing RL methods.To address this issue, we introduce NLPO (Natural Language Policy Optimization), which is inspired by work on action elimination/invalid-action masking (Zahavy et al., 2018; Huang & Ontañón, 2020; Ammanabrolu & Hausknecht, 2020). NLPO, a parameterized-masked extension of PPO, learns to mask out less relevant tokens in-context as it trains. NLPO accomplishes this via top-p sampling, which restricts tokens to the smallest possible set whose cumulative probability is greater than the probability parameter p (Holtzman et al., 2018).RL for Large Action Spaces. MIXER (Ranzato et al., 2015) combined ideas from schedule sampling and REINFORCE (Williams, 1992).Bahdanau et al. (2016) proposed an actor-critic algorithm to address the variance/large action space problems when using REINFORCE for language generation; follow-up works such asKG-A2C (Ammanabrolu & Hausknecht, 2020), TrufLL (Martin et al., 2022), AE-DQN (Zahavy et al., 2018), and GALAD (Ammanabrolu et al., 2022) addressed similar issues by attempting to eliminate and reduce the action space during exploration.Each environment is an NLP task: we are given a supervised dataset \mathcal{D}=\{({\bm{x}}^{i},{\bm{y}}^{i})\}_{i=1}^{N} of N examples, where {\bm{x}}\in\mathcal{X} is an language input and {\bm{y}}\in\mathcal{Y} is the target string. Generation can be viewed as a Markov Decision Process (MDP) \langle\mathcal{S},\mathcal{A},\mathcal{R},P,\gamma,T\rangle using a finite vocabulary \mathcal{V}.Each episode in the MDP begins by sampling a datapoint ({\bm{x}},{\bm{y}}) from our dataset and ends when the current time step t exceeds the horizon T or an end of sentence (EOS) token is generated.The input {\bm{x}}=(x_{0},\cdots,x_{m}) is a task-specific prompt that is used as our initial state \bm{s}_{0}=(x_{0},\cdots,x_{m}), where \bm{s}_{0}\in\mathcal{S} and \mathcal{S} is the state space with x_{m}\in\mathcal{V}. An action in the environment a_{t}\in\mathcal{A} consists of a token from our vocabulary \mathcal{V}. The transition function P:\mathcal{S}\times\mathcal{A}\rightarrow\mathcal{S} deterministically appends an action a_{t} to the end of the state \bm{s}_{t-1}=(x_{0},\cdots,x_{m},a_{0},\cdots,a_{t-1}). This continues until the end of the horizon t\leq T and we obtain a state \bm{s}_{T}=(x_{0},\cdots,x_{m},a_{0},\cdots,a_{T}).At the end of an episode a reward \mathcal{R}:\mathcal{S}\times\mathcal{A}\times\mathcal{Y}\rightarrow\mathbb{R}^{1} that depends on the (\bm{s}_{T},{\bm{y}}) (e.g., an automated metric like PARENT Dhingra et al. (2019)) is emitted. RL4LMs provides an OpenAI gym (Brockman et al., 2016) styleAPI for an RL environmentthat simulates this LM-Based MDP formulation.Abstracting the details of the MDP environment structure allows for new tasks to be added quickly with compatibility across all implemented algorithms.	The action space for language modeling is equal to the vocabulary set of language models.  Since the vocabularies are very large (i.  tens of thousands of possible tokens), the action space is also very large.  In general, locomotion in the real world can be condensed to five quantities - moving across X, Y or Z axes, or a linear combination thereof.  The authors mention that typical RL problems have an action space that is an order of magnitude smaller, but do not specifically explain how the action spaces for typical problems is modeled or constructed.	Change Number